Skip to main content
QMSQMS
QMS
  • Welcome to your QMS
  • Quality Manual
  • Procedures
  • Records
  • TF_Legit.Health_Plus
    • Legit.Health Plus TF index
    • Legit.Health Plus STED
    • Legit.Health Plus description and specifications
    • R-TF-001-007 Declaration of conformity
    • GSPR
    • Clinical
      • Evaluation
        • R-TF-015-001 Clinical Evaluation Plan_2023_001
        • R-TF-015-002 Preclinical and clinical evaluation record_2023_001
        • R-TF-015-003 Clinical Evaluation Report_2023_001
        • R-TF-015-007 Delaration of interest Clinical evaluation team_2023_001
        • R-TF-015-007 Delaration of interest Clinical evaluation team_2023_002
        • R-TF-015-007 Delaration of interest Clinical evaluation team 2023_003
        • R-TF-015-007 Delaration of interest Clinical evaluation team 2023_004
        • R-TF-015-007 Delaration of interest Clinical evaluation team 2023_005
        • R-TF-015-007 Delaration of interest Clinical evaluation team 2023_006
      • Investigation
      • R-TF-015-008 Clinical development plan_2023_002
    • Design and development
    • Design History File (DHF)
    • IFU and label
    • Post-Market Surveillance
    • Quality control
    • Risk Management
  • Licenses and accreditations
  • External documentation
  • Procedures
  • Records
  • TF_Legit.Health_Plus
  • Clinical
  • Evaluation
  • R-TF-015-003 Clinical Evaluation Report_2023_001

R-TF-015-003 Clinical Evaluation Report_2023_001

Table of contents

  • Scope
  • Summary
  • Scope of the Clinical Evaluation
    • Manufacturer contact details
  • Product characterization
  • Intended use or purpose
  • Variants and models
  • Expected lifetime
  • Clinical Benefits, Outcome Parameters
    • Comprehensive Dermatological Data for Informed Clinical Decisions. Comprehensive Analysis for Informed Decision Making. Improvement in Dermatological Assessment Accuracy. Precise Detection of Dermatological Features. Empowerment of Health Care Practitioners.
    • Faster Measurement of Clinical Signs. Accurate and Objective Measurement of Clinical Signs. Precise Quantification, Count and Extent measure of Skin Issues. Facilitation of Longitudinal Skin Condition Monitoring. Consistent Tracking of Patient Condition Over Time. More agile follow-up consultations.
    • Targeted Analysis for Facial Palsy Evaluation. Support in Assessing Facial Palsy. Accurate Assessment for Facial Palsy.
    • Improved Operational Efficiency for Healthcare Organizations. Streamlining Healthcare Operations. Data-Driven Insights for Workflow Optimization.
    • Support in Preliminary ICD Classification through Image Analysis. Aiding in ICD Classification. Diagnostic Support.
  • Claims foreseen by the manufacturer
    • Clinical performance claims
    • Clinical safety claims
  • Physical and chemical description
  • Incorporation of medicinal substances, tissues or blood products
  • Mechanical and physicochemical characteristics
  • Other characteristics
  • Device pictures, drawings and/or schemes
  • Technologies used
    • Quantification of intensity, count and extent of visible clinical signs
    • Image-based recognition of visible ICD classes
    • Image quality assessment
    • Image domain check
  • Intended application of device
  • Applicable standards and regulations
    • Regulations
    • Common specifications
    • Harmonized standards
  • Available options
    • Dermatological assessment
    • Facial Palsy Assessment
    • Conclusion
  • Clinical Safety, Methods for Analysis
    • Safety-related product claims
  • Benefits and risks. Acceptability of Benefit-Risk-Ratio and undesirable side-effects
    • Benefits
    • Risks and undesirable side-effects
    • Acceptability of Benefit-Risk ratio
    • Conclusion
  • Risk Analysis and minimization and management of side effects and other risks
  • Type of evaluation of the device
  • Clinical Background, Current Knowledge, State of the Art
    • Clinical Background & Current Knowledge
    • Literature Search strategy
  • Device equivalence
    • 1. Technical characteristics
    • 2. Biological characteristics
    • 3. Clinical Characteristics
    • Summary
  • Data generated and held by the manufacturer
    • Technical and pre-clinical data generated and held by us
    • Clinical data generated and held
  • Clinical Data
    • Clinical Data From Literature
    • Appraisal of clinical data
    • Clinical Data from Clinical Study Databases
    • Clinical Data From Adverse Event Databases
    • Analysis of the Clinical Data
  • Post-Market Activities
    • Reference to Key Documents and Records
    • Data Tracking for Clinical Benefits and Safety Verification
  • GAP analysis
    • Validation and Evidence of Clinical Safety and Performance
    • Relation with Market Experience and Literature
    • PMCF Activities
  • Conclusions
    • Validation of Safety and Performance
    • Validation of User Information
    • Conclusion Table for GSPR
  • Date of the Next Clinical Evaluation
  • Qualification of the Responsible Evaluators
    • Clinical evaluation team justification
  • Dates and signatures
  • Record signature meaning

Scope​

This Clinical Evaluation Report describes the clinical benefits and safety characteristics of the device, based on clinical data. It is the output of the T-015-001 Clinical Evaluation Plan.

Furthermore, this Clinical Evaluation Report serves as evidence of conformance with certain General Safety and Performance Requirements pursuant to EU Regulation 2017/745 (MDR), Annex I.

Specifically, the following requirements were evaluated as part of this report:

  • Chapter 1 (General Requirements), sections 1 and 8
  • Chapter 2.8 (Software Devices), section 17
  • Chapter 3 (product information), section 23.4

Summary​

The clinical evaluation report presented herein provides a comprehensive analysis of the device, a cutting-edge tool designed for medical assessment of images representing skin structures. The device utilizes advanced artificial intelligence algorithms to aid healthcare professionals in the diagnosis and monitoring of various skin conditions.

The benefit/risk profile of the device has been extensively studied across diverse target groups and medical indications pertinent to dermatology. The device has demonstrated a remarkable ability to provide accurate, reliable, and timely evaluations, thereby significantly aiding in the early detection and management of dermatological diseases.

Through clinical appraisals, real-world evidence, and a thorough examination of the current state of the art, the device has established its place as a reliable and valuable tool in the medical field. It addresses a critical need for precision and efficiency in dermatological assessments, offering substantial benefits over traditional methods.

The safety of the device has been a paramount concern throughout its development and evaluation. The identified risks have been meticulously managed and mitigated, ensuring that the overall benefit/risk ratio remains highly favorable. Special attention has been given to ensure the device's performance is consistent, and its results are clinically relevant and reliable.

In conclusion, the device stands as a testament to the potential of integrating artificial intelligence in medicine, offering transformative benefits to both healthcare providers and patients. Its development and evaluation have been conducted with the diligence, ensuring that it meets and exceeds the required safety and performance standards, aligning with the current state of the art in dermatology.

Scope of the Clinical Evaluation​

Manufacturer contact details​

Manufacturer data
Legal manufacturer nameAI Labs Group S.L.
AddressStreet Gran Vía 1, BAT Tower, 48001, Bilbao, Bizkaia (Spain)
SRNES-MF-000025345
Person responsible for regulatory complianceAlfonso Medela, María Diez, Giulia Foglia
E-mailoffice@legit.health
Phone+34 638127476
TrademarkLegit.Health

Product characterization​

Information
Device nameLegit.Health Plus (hereinafter, the device)
Model and typeNA
Version1.0.0.0
Basic UDI-DI8437025550LegitCADx6X
Certificate number (if available)MDR 792790
EMDN code(s)Z12040192 (General medicine diagnosis and monitoring instruments - Medical device software)
GMDN code65975
ClassClass IIb
Classification ruleRule 11
Novel product (True/False)FALSE
Novel related clinical procedure (True/False)FALSE
SRNES-MF-000025345

Intended use or purpose​

Intended use​

The device is a computational software-only medical device intended to support health care providers in the assessment of skin structures, enhancing efficiency and accuracy of care delivery, by providing:

  • quantification of intensity, count, extent of visible clinical signs
  • interpretative distribution representation of possible International Classification of Diseases (ICD) classes.

Quantification of intensity, count and extent of visible clinical signs​

The device provides quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others; including, but not limited to:

  • erythema,
  • desquamation,
  • induration,
  • crusting,
  • dryness,
  • oedema,
  • oozing,
  • excoriation,
  • swelling,
  • lichenification,
  • exudation,
  • depth,
  • edges,
  • undermining,
  • pustulation,
  • hair loss,
  • type of necrotic tissue,
  • amount of necrotic tissue,
  • type of exudate,
  • peripheral tissue edema,
  • peripheral tissue induration,
  • granulation tissue,
  • epithelialization,
  • nodule count,
  • papule count,
  • pustule count,
  • cyst count,
  • comedone count,
  • abscess count,
  • draining tunnel count,
  • lesion count

Image-based recognition of visible ICD classes​

The device is intended to provide an interpretative distribution representation of possible International Classification of Diseases (ICD) classes that might be represented in the pixels content of the image.

Device description​

The device is computational software-only medical device leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures. Its principal function is to provide a wide range of clinical data from the analyzed images to assist healthcare practitioners in their clinical evaluations and allow healthcare provider organisations to gather data and improve their workflows.

The generated data is intended to aid healthcare practitioners and organizations in their clinical decision-making process, thus enhancing the efficiency and accuracy of care delivery.

The device should never be used to confirm a clinical diagnosis. On the contrary, its result is one element of the overall clinical assessment. Indeed, the device is designed to be used when a healthcare practitioner chooses to obtain additional information to consider a decision.

Intended medical indication​

The device is indicated for use on images of visible skin structure abnormalities to support the assessment of all diseases of the skin incorporating conditions affecting the epidermis, its appendages (hair, hair follicle, sebaceous glands, apocrine sweat gland apparatus, eccrine sweat gland apparatus and nails) and associated mucous membranes (conjunctival, oral and genital), the dermis, the cutaneous vasculature and the subcutaneous tissue (subcutis).

Intended patient population​

The device is intended for use on images of skin from patients presenting visible skin structure abnormalities, across all age groups, skin types, and demographics.

Intended user​

The medical device is intended for use by healthcare providers to aid in the assessment of skin structures.

User qualification and competencies​

In this section we specificy the specific qualifications and competencies needed for users of the device, to properly use the device, provided that they already belong to their professional category. In other words, when describing the qualifications of HCPs, it is assumed that healthcare professionals (HCPs) already have the qualifications and competencies native to their profession.

Healthcare professionals​

No official qualifications are needes, but it is advisable if HCPs have some competencies:

  • Knowledge on how to take images with smartphones.
IT professionals​

IT professionals are responsible for the integration of the medical device into the healthcare organisation's system.

No specific official qualifications are needed, but it is advisable that IT professionals using the device have the following competencies:

  • Basic knowledge of FHIR
  • Understanding of the output of the device.

Use environment​

The device is intended to be used in the setting of healthcare organisations and their IT departments, which commonly are situated inside hospitals or other clinical facilities.

The device is intended to be integrated into the healthcare organisation's system by IT professionals.

Operating principle​

The device is computational medical tool leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures.

Body structures​

The device is intended to use on the epidermis, its appendages (hair, hair follicle, sebaceous glands, apocrine sweat gland apparatus, eccrine sweat gland apparatus and nails) and associated mucous membranes (conjunctival, oral and genital), the dermis, the cutaneous vasculature and the subcutaneous tissue (subcutis).

In fact, the device is intended to use on visible skin structures. As such, it can only quantify clinical signs that are visible, and distribute the probabilities across ICD classes that are visible.

Variants and models​

The device does not have any variants.

Expected lifetime​

Its expected lifetime is considered unlimited because the device will be updated with each improvement opportunity extracted from the information and analysis of the data provided by the continuous and systematic post-market data follow-up.

Clinical Benefits, Outcome Parameters​

This section provides an analysis of the clinical benefits and outcome parameters associated with the device, which have been extracted from the evidence available at the record R-TF-015-002.

Comprehensive Dermatological Data for Informed Clinical Decisions. Comprehensive Analysis for Informed Decision Making. Improvement in Dermatological Assessment Accuracy. Precise Detection of Dermatological Features. Empowerment of Health Care Practitioners.​

  • Performance: The device provides an analysis of the epidermis, dermis, and their appendages, delivering a wide array of clinical data to support healthcare practitioners in their evaluations. Utilizing advanced computer vision algorithms, the device detects and analyses various dermatological features, providing detailed insights into the condition of the skin, that practitioners use to improve their clinical assessment.
  • Evidence: Validation studies and real-world applications have demonstrated the device's proficiency in helping practitioners increase their diagnostic success rate, thereby facilitating a more comprehensive and informed clinical decision-making process.

Faster Measurement of Clinical Signs. Accurate and Objective Measurement of Clinical Signs. Precise Quantification, Count and Extent measure of Skin Issues. Facilitation of Longitudinal Skin Condition Monitoring. Consistent Tracking of Patient Condition Over Time. More agile follow-up consultations.​

  • Performance: The tool provides objective quantification of various clinical signs such as erythema, desquamation, and induration, offering precise measurements that enhance the evaluation and monitoring of skin conditions. More concretely, the device offers precise quantification of the intensity, count, and extent of various clinical signs. By doing so, the device is specifically designed to facilitate the longitudinal monitoring of skin conditions, allowing healthcare practitioners to track changes and progression over time with high precision. And not only that, but it achieves the results in a matter of seconds.
  • Evidence: The vast literature shows the high inter-observer variability of HCPs in estimating the intensity, extent and count of clinical signs. The issue of subjectivity is so pronounced that there is even a high intra-observer variability. On the contrary, our validation has consistently shown the device's ability to provide accurate and reliable quantifications, reducing inter-observer variability, and ensuring that healthcare practitioners have access to objective data for improved patient care. Furthermore, the device outputs the score in a range from 1 to 100, which improves the Minimum Observable Change by providing a higher Level of Differentiation (LoD). And most importantly, the evidence shows that the device is a method that is much faster and less time-consuming for HCPs.

Targeted Analysis for Facial Palsy Evaluation. Support in Assessing Facial Palsy. Accurate Assessment for Facial Palsy.​

  • Performance: When it comes to assessing facial palsy, the device offers targeted and specialized data, aiding healthcare practitioners in making accurate and timely evaluations of the intensity of facial nerve injury. Thus, the device aids in the assessment process by providing detailed data specific to facial structures. And not only that, but it achieves the results in a matter of seconds.
  • Evidence: Studies focused on facial palsy assessment have highlighted the device's effectiveness in providing critical data necessary for a comprehensive evaluation, showcasing its utility in this specific clinical scenario. The vast literature shows the high inter-observer variability of HCPs in estimating the degree of the affectation of facial nerve injury, which is subject to high subjectivity. On the contrary, our validation has consistently shown the device's ability to provide accurate and reliable quantifications, reducing inter-observer variability, and ensuring that healthcare practitioners have access to objective data for improved patient care. And most importantly, the evidence shows that the device is a method that is much faster and less time-consuming for HCPs.

Improved Operational Efficiency for Healthcare Organizations. Streamlining Healthcare Operations. Data-Driven Insights for Workflow Optimization.​

  • Performance: The device not only aids in clinical evaluations but also provides actionable insights that contribute to the optimization of clinical workflows within healthcare organizations. By delivering quick and precise clinical data, the device plays a crucial role in enhancing the operational efficiency of healthcare workflows, leading to better patient outcomes and resource management.
  • Evidence: Feedback from various healthcare organizations and practitioners confirms the device's positive impact on streamlining workflows and improving the overall efficiency of patient care delivery in tasks such as increasing the adequacy of referrals or improving triaging patients. Performance metrics have illustrated the device's role in enhancing workflow efficiency, leading to improved patient care and operational effectiveness.

Support in Preliminary ICD Classification through Image Analysis. Aiding in ICD Classification. Diagnostic Support.​

  • Performance: The device offers an interpretative distribution representation of potential ICD classes based on image analysis, providing valuable preliminary information that can aid in the patient management process, aiding in the preliminary categorization of conditions.
  • Evidence: Through extensive testing and real-world application, the device has proven its capability to accurately suggest potential ICD classes, supporting healthcare practitioners in the early stages of diagnosis and treatment planning. Validation studies have showcased the device's capability to accurately represent ICD classes, assisting practitioners in the early stages of diagnosis and patient management.

Claims foreseen by the manufacturer​

Clinical performance claims​

  • Similar performance in comparison with similar devices analyses in preclinical data.
  • Design based on harmonised standards.
  • Absence of known adverse events.
  • Acceptable benefit/risk ratio.
  • Clinical performance confirmation by clinical data (included PMCF).

Clinical safety claims​

  • Clinical safety validated by similar devices.
  • Design based on harmonised standards.
  • Absence of known adverse events.
  • Acceptable benefit/risk ratio.
  • Clinical safety confirmation by clinical data (included PMCF).

Physical and chemical description​

This section does not apply in this case as the product is a standalone software.

Incorporation of medicinal substances, tissues or blood products​

This section does not apply in this case as the product is a standalone software.

Mechanical and physicochemical characteristics​

This section does not apply in this case as the product is a standalone software.

Other characteristics​

The device has been developed to improve the current state of the art to process photographs of skin structure and then processes them with artificial intelligence algorithms.

Device pictures, drawings and/or schemes​

The following diagram illustrates the basic architecture:

It is worth mentioning that the interaction between the elements is conceptualised as element itself. This is the orchestrator. Indeed, the orchestrator is the component of the architecture that contains the logic of how the different components interact with each other.

Technologies used​

The device is the result of an incremental improvement of an existing technology. As we have previously mentioned, it has been developed to improve the current state of the art to process photographs of skin structure and then processes them with artificial intelligence algorithms.

Quantification of intensity, count and extent of visible clinical signs​

One of the core features of the device is to provide a quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others.

To achieve that, the device uses a range of deep learning technologies, combined and developed for that specific use. Here's a list of some of the technologies used:

  • Object detection: used to count clinical signs such as hives, papules or nodules.
  • Semantic segmentation: used to determine the extent of clinical signs such as hair loss or erythema.
  • Image recognition: used to quantity the intensity of visual clinical signs like erythema, excoriation, dryness, lichenification, oozing, and edema.

The device is trained on specific subsets of our global skin image dataset and validated for the specific tasks they have been trained on.

Image-based recognition of visible ICD classes​

Another core feature of the device is a deep learning-based image recognition technology for the recognition of ICD classes. In other words: when the device is fed an image or a set of images, it outputs an interpretative distribution representation of possible International Classification of Diseases (ICD) classes that might be represented in the pixels content of the image.

The device makes its prediction entirely based on the visual content of the images, with no additional parameters.

The device has been developed following an architecture called Vision Transformer (ViT). This architecture is inspired in the Transformer architecture, which is extensively used in other areas such as NLP and has brought significant advancements in terms of performance.

Transformer architecture

A more detailed explanation of this architecture can be found in the publication "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" (Dosovitskyi et al., 2020).

Data collection, model training and validation​

In order to develop this image recognition feature, we created an image dataset by collecting images from diverse sources, including the most famous skin image datasets, which included the corresponding confirmed ICD class for every image. This results in a dataset of near 1000 different classes and a diverse representation of age, sex and skin tone. To ensure the model has enough images per category, only classes with a sufficient number of images are considered for training the model.

One crucial step of the development is splitting the dataset into three groups:

  1. Training sets
  2. Validation sets
  3. Test sets

When an incoming image dataset includes any sort of metadata that makes it possible to group images by subject, the data is split at subject level. This strategy improves the reliability of the validation and test metrics, and it is a best practice in the field.

Thanks to such a large collection of datasets, it is also possible to perform reliable validations by reserving some of these datasets entirely for testing, which helps explore and analyze the performance of the model in completely uncontrolled scenarios.

Model calibration​

Image recognition models are known for becoming overly confident after training, which, applied to this scenario, can deter the overall ICD classification performance. If the device predicts the wrong probabilities and sets an extremely high probability for an incorrect class, the answer may lead the user to believe the model is confident about the answer.

To overcome this problem, a final step has been added to the training process, which consists of slightly fitting the model to the validation set, applying temperature scaling. By applying this additional postprocessing, the device is enforced to generate 'softer' or less extreme probability distributions.

The main benefit of model calibration is that it increases the interpretability of the results, as models not only need to be accurate (i.e. the correct class should be within the top 3 or 5 classes with the highest probability in the distribution) but also indicate how likely it is that the output is not correct. When the model is fed with an image and the output is a very soft distribution, it means that the model is not confident enough about any particular ICD class.

Calibration

A more detailed explanation can be found in the publication "On calibration of modern neural networks" (Guo et al., 2017).

Test-time augmentation (TTA)​

In addition to using a state-of-the-art architecture, we apply test-time augmentation (TTA) to obtain a more stable output. This technique consists of applying slight distortions to the input image (such as changing the contrast and brightness, rotating the image or equalizing the histogram of the image), feeding the model with the input image and all its distorted views. This results in one probability distributions per image. Finally, all the probability distributions are averaged to obtain the final output.

Image quality assessment​

All the aforementioned deep learning models are expected to be used with images that contain actual skin structure captured with at least some level of visual quality. In other words, an input image must meet a minimum standard in terms of photography-related parameters such as exposure, focus, contrast or resolution. If these standards are not met when the user submits an image, there is the risk that the models produce unreliable predictions.

In order to ensure the optimal visual quality and clinical utility of the images, we use a different deep learning technology, that we calle DIQA (Dermatology Image Quality Assessment).

DIQA is used to predict the perceived visual quality of every single image before it being processed by the device. By using this feature, it is possible to spot images that are unsuitable for the intended use, and therefore guarantee that only the best images are utilized.

Dermatology Image Quality Assessment (DIQA)

For more information, read DIQA's research letter "Dermatology Image Quality Assessment (DIQA): Artificial intelligence to ensure the clinical utility of images for remote consultations and clinical trials" (Hernández-Montilla et al., 2023).

To train this model, a dataset of skin images was assessed by a large crowd of human observers, following the ITU-T P.910 Recommendations, which were tasked to review every image and assign a quality score that ranged from 0 (the worst quality) to 10 (the best quality). The model, based on the EfficientNet architecture, was then trained using the average score of every image as labels.

Image domain check​

In some cases, the user may accidentally upload an image that, no matter the visual quality, does not represent skin structure. Examples of such out-of-domain images are photographs of other organs, out-of-scope body structures, printed reports, screenshots of medical records, a picture of a surgical tool, or more extreme examples such as a picture of a dog or a truck.

To prevent the misuse of the device, it incorporates a deep learning model for image domain check. It consists of a lightweight image recognition model (a tiny ViT) trained to classify an input image as clinical, dermoscopic, and out-of-domain. Thanks to the low complexity of this task (i.e. it is easy to distinguish between a dermatology image and a natural image), it is possible to train a model that is not computationally expensive, yet with a high performance, which adds an extra level of input quality assurance without any cost in terms of overall processing time.

Intended application of device​

Due to the nature of the device being a standalone software, it is indicated to use it indefinitely, always updating the latest version of the device according to our communications and indications.

Applicable standards and regulations​

Regulations​

  • Regulation (EU) 2017/745 of the European Parliament and of the Council on Medical Devices.
  • Regulation (EU) 2023/607 of the European Parliament and of the Council of 15 March 2023 amending Regulations (EU) 2017/745 and (EU) 2017/746 as regards the transitional provisions for certain medical devices and in vitro diagnostic medical devices.
  • Spanish Royal Decree 192/2023 on Medical Devices.

Common specifications​

  • Commission Implementing Regulation (EU) 2021/2226 of 14 December 2021 laying down rules for the application of Regulation (EU) 2017/745 of the European Parliament and of the Council as regards electronic instructions for use of medical devices.
    • As the device is a software, the IFU are provided electronically. We prepare them following this regulation and include its reference at the device Declaration of Conformity.
  • Commission Implementing Regulation (EU) 2021/2078 of 26 November 2021 laying down rules for the application of Regulation (EU) 2017/745 of the European Parliament and of the Council as regards the European Database on Medical Devices (Eudamed).
    • We have followed the regulation and registered our company within Eudamed, obtaining our SRN code. We will also register the device once we obtain the CE mark before the mandatory period starts.
  • Commission Implementing Decision (EU) 2019/939 of 6 June 2019 designating issuing entities designated to operate a system for the assignment of Unique Device Identifiers (UDIs) in the field of medical devices.
    • According to this regulation, we have follow the GS1 international organisation to assign the device UDI.

Harmonized standards​

  • UNE-EN ISO 13485:2016 Medical devices. Quality management systems. Requirements for regulatory purposes.
  • ISO 14971:2020 Medical devices.Application of risk management to medical devices
  • ISO 15223-1:2021 Medical devices - Symbols to be used with information to be supplied by the manufacturer - Part 1: General requirements
  • UNE-EN 62304:2007/A1:2016 Medical device software - Software life-cycle processes
  • UNE-EN 62366-1:2015/A1:2020 Medical devices - Part 1: Application of usability engineering to medical devices
  • UNE-EN ISO 14155:2021 Clinical investigation of medical devices for human subjects - Good clinical practice (ISO 14155:2020)

According to the last Commission implementing decisions containing references to the harmonised standards that apply to us:

  • Commission implementing decision (EU) 2021/1182
  • Commission implementing decision (EU) 2022/6
  • Commision implementing decision (EU) 2023/694

Available options​

In the realm of dermatological and facial palsy assessment, numerous therapeutic, management, and diagnostic options have been developed over the years. This section provides an overview of these options, their historical context, and a summary of their respective advantages and disadvantages.

Dermatological assessment​

Measuring the severity​

Clinical scales (e.g., PASI, SCORAD...)​

Used to grade the severity of dermatoses.

  • Advantages: Simple, quick, and no special equipment required.
  • Disadvantages: Subjective and may not capture subtle changes in condition.
Incremental improvement

The device actually uses these pre-existing and widely-used clinical scales to output the information about the intensity, count and extent of clinical signs. In this sense, the device does not disrupt the use of clinical scales. On the contrary, it mitigates their disadvantages by maintaining their basic functioning.

Diagnosing​

Traditional dermatological assessments have heavily relied on visual inspection and the clinical experience of healthcare practitioners. Over time, various tools and technologies have been introduced to enhance the accuracy and efficiency of these assessments.

Dermoscopy​

A technique involving the examination of skin lesions with a dermoscope.

  • Advantages: Improved visualization of subsurface skin structures, which are not visible to the naked eye. It allows for a more accurate diagnosis of melanoma and other skin cancers.
  • Disadvantages: Requires extensive training and experience to interpret results accurately.
Works with Dermoscopy

It is worth mentioning that the device also works with dermoscopic image. In other words: the device does not necessarily replace the dermoscopy.

Skin Biopsy​

A procedure where a sample of skin is removed for laboratory analysis.

  • Advantages: Provides a definitive diagnosis and is particularly useful for identifying cancerous cells.
  • Disadvantages: Invasive, may cause scarring, and results can take time to be processed.
Relationship between biopsy

It is worth mentioning that the device does not replace the biopsy. Indeed, whenever the HCP suspects that the lesion may be malignant, specially if its melanoma, the biopsy happens regardless of whether or not the practitioner used the device during their assessment.

Other imaging techniques (e.g., MRI, CT scans)​

Used to get detailed images of the skin and underlying structures.

  • Advantages: Non-invasive and provides a comprehensive view of the affected area.
  • Disadvantages: Expensive, not always readily available, and may not always provide sufficient information for a complete diagnosis.

Therapeutic options​

Not comparable with the device

This section explains some of the therapies available in dermatology. However, keep in mind that the device is in no way a therapy, so it does not replace in any form or manner the following options.

Topical Treatments​

Creams, lotions, and ointments applied directly to the skin.

  • Advantages: Easy to apply, less invasive.
  • Disadvantages: May not be as effective for severe or deep-seated skin conditions.
Systemic Medications​

Oral or injectable drugs used to treat a variety of skin conditions.

  • Advantages: Can address issues that are not treatable with topical treatments.
  • Disadvantages: May have more systemic side effects.
Phototherapy​

Using ultraviolet light to treat certain skin conditions.

  • Advantages: Non-invasive and effective for certain conditions like psoriasis.
  • Disadvantages: Requires multiple sessions, and long-term safety is still being studied.

Facial Palsy Assessment​

Clinical Scales (e.g., House-Brackmann scale)​

Used to grade the severity of facial palsy.

  • Advantages: Simple, quick, and no special equipment required.
  • Disadvantages: Subjective and may not capture subtle changes in condition.
Incremental improvement

The device actually uses these pre-existing and widely-used clinical scales to output the information about the intensity of the facial palsy. In this sense, the device does not disrupt the use of clinical scales. On the contrary, it mitigates their disadvantages by maintaining their basic functioning.

Diagnosing​

Facial palsy, characterized by weakness or paralysis of the facial muscles, has traditionally been assessed through clinical examination. With advancements in technology, more options have become available.

Electromyography (EMG)​

Measures the electrical activity of muscles.

  • Advantages: Provides objective data on muscle function.
  • Disadvantages: Can be uncomfortable; interpretation of results requires specialized knowledge.
Imaging techniques (e.g., MRI, CT scans)​

Used to view the facial nerves and structures.

  • Advantages: Non-invasive and provides detailed information.
  • Disadvantages: Expensive and may not always pinpoint the exact issue.

Conclusion​

While there are numerous options available for the assessment and management of dermatological conditions and facial palsy, each has its own set of advantages and disadvantages. The introduction of computational tools leveraging computer vision represents a significant leap forward, offering non-invasive, accurate, and efficient ways to enhance clinical decision-making and patient care.

Clinical Safety, Methods for Analysis​

The methods used to evidence the medical device clinical safety are based on preclinical and clinical data analysis, and the acceptability of the benefit/risk ratio derived from this analysis.

The parameters used to determine, based on the state of the art, the global acceptability of the benefit/risk ratio for the intended purpose of the device are:

  • No adverse effects should be found in the preclinical data evaluation.
  • Current literature should support the usage of AI on the manufacturing process and should evidence the clinical benefits of the use of the device.
  • Current literature should also show the feasibility of the device in comparison with current alternatives and solutions. The advantages of the algorithm performance (PMCF activity 2) should be clear.
  • No significant risks should be identified in relation to the patients safety.
  • The positive impact of the device should be strongly evidenced on the algorithm performance and the feedback obtained (PMCF activities 2 and 4).
  • The use of the device as a skin conditions diagnosis support tool, should be proven necessary due to its multiple advantages. Based on the device's characteristics, the device should not have side-effects specifically related to its use and should have a high benefit/risk ratio.

We have a risk management plan in place, defined in GP-013 Risk management, to perform the risk assessment and create the required T-013-003 Risk management report according to the EN ISO 14971:2019 to evaluate all the known and foreseeable risks related to the product.

Safety-related product claims​

It is not known or foreseen any undesirable side-effects specifically related to the use of the software.

Benefits and risks. Acceptability of Benefit-Risk-Ratio and undesirable side-effects​

In this section, we reevaluate the benefit-risk ratio of the device, taking into account the diverse clinical contexts, user profiles, and potential limitations of the device.

Benefits​

Clinical advantages​

The device offers precision in analyzing skin structures and assessing facial palsy, contributing significantly to improved clinical outcomes. The non-invasive nature of the tool ensures patient safety while providing healthcare practitioners with a wealth of data to aid in their clinical evaluations.

Efficiency and workflow improvement​

By seamlessly integrating into existing healthcare workflows, the device enhances operational efficiency, reduces patient wait times, and optimizes resource utilization within healthcare organizations.

Support for longitudinal monitoring​

The ability to track the progression of skin conditions over time allows for more personalized and effective patient care, as well as providing invaluable data for long-term clinical studies.

Preliminary diagnostic assistance​

Our tool offers preliminary interpretative distribution representation of possible ICD classes, aiding in the initial stages of patient evaluation and contributing to a more streamlined diagnostic process.

Risks and undesirable side-effects​

While the device presents clinical benefits, it is crucial to remain cognizant of potential risks and undesirable side-effects.

Data privacy and security​

As a software device handling sensitive patient data, ensuring the utmost data privacy and security is paramount. Any breach or misuse of data could have severe implications for patient privacy and trust in the device.

Over-reliance on the tool​

There is a potential risk of healthcare practitioners becoming overly reliant on the device, potentially neglecting other crucial aspects of patient evaluation. It is vital to reinforce that our tool is intended to be an aid in the clinical decision-making process, not a replacement.

Misinterpretation of data​

Given the complexity of the data provided by the device, there is a risk of misinterpretation, which could lead to incorrect clinical decisions. Proper training and clear guidelines for use are essential to mitigate this risk.

Acceptability of Benefit-Risk ratio​

Criteria for acceptance​

Our acceptable benefit-risk profile hinges on the device demonstrating clear clinical advantages, efficiency improvements, and support for longitudinal monitoring, with minimal risks associated with data security, over-reliance, and data misinterpretation.

  • Clinical safety: No adverse effects should be found in preclinical data evaluation, and a positive impact on algorithm performance and user feedback should be evident (as per PMCF activities 2 and 4).
  • Literature support: Current literature should support the use of AI in the manufacturing process, highlighting the clinical benefits and advantages over existing alternatives.
  • Risk management: A comprehensive risk management plan (GP-013 Risk management) is in place, ensuring all known and foreseeable risks are evaluated and mitigated.

Target populations and clinical contexts​

The device is designed for use across all age groups, skin types, and demographics, ensuring wide applicability. In the context of facial palsy, the device targets patients presenting with facial nerve injury. It is imperative that the benefits and risks are carefully weighed in these diverse clinical scenarios to maintain a favorable benefit-risk ratio.

Professional opinions and unmet medical needs​

Diverging opinions among healthcare professionals regarding the use of computational tools in clinical settings necessitate clear communication of the device's benefits and limitations. Addressing unmet medical needs, particularly in the realm of dermatological assessment and facial palsy evaluation, remains a top priority.

Conclusion​

In conclusion, our computational medical tool presents a highly favorable benefit-risk ratio, with its myriad of clinical benefits significantly outweighing the potential risks and undesirable side-effects. Continuous monitoring, proper user training, and adherence to data privacy and security protocols are essential to maintaining this balance and ensuring the ongoing safety and efficacy of the device.

Risk Analysis and minimization and management of side effects and other risks​

The current state of risks control verifies that all foreseeable risks and residual risks are controlled according to R-TF-013-002 Risk Management Record_2023_001.

All risks are in an acceptable risk zone, but the individual risks mentioned below, are defined as AFAP risk level.

#Hazard and hazardous situationAdditional control measures
5The medical device outputs a wrong result to the care providerWe specify in the intended purpose of the device that is a support tool, not a diagnosis one, meaning that it must always be used by HCP, who should confirm or validate the results obtained also considering the medical history of the patient and other possible sympthom they could be suffering.
6The medical device outputs a wrong result to the physicianWe specify in the intended purpose of the device that is a support tool, not a diagnosis one, meaning that it must always be used by HCP, who should confirm or validate the results obtained also considering the medical history of the patient and other possible sympthom they could be suffering.
9The quality of the image is not high enough for the service to perform correctlyWe offer training to the users to optimize the imaging process so that it is optimal for the device's operation.
30The user is unable to provide adequate lighting conditions for the image to be viewableWe offer training to the users to optimize the imaging process so that it is optimal for the device's operation.

Type of evaluation of the device​

As we explain at the R-TF-015-001 Clinical evaluation plan_2023_001, three key components are taken into consideration to analyze the clinical evidence of the device:

  • Valid clinical association: the extent to which the software's output, based on the inputs and algorithms selected, is associated with the targeted physiological state or clinical condition defined in the intended purpose.
    • Evidence generated through the previous literature research, proof of concept studies or manufacturer's own clinical investigations/clinical performance studies.
  • Technical performance: ability to accurately, reliably and precisely generate the intended output from the input data (principle of operation).
    • Evidence generated through verification and validation activities.
  • Clinical performance: ability to yield clinically relevant output in accordance with the intended purpose. It assesses clinical safety, effectiveness and performance. It supports the demonstration of clinical benefits against foreseeable or known risks associated with the product (benefit/risk ratio acceptability) and compared with current processes, expected benefits and diagnostic/therapeutic/monitoring improvements.
    • Evidence generated through device equivalence in the target population and for the intended use, contraindications and undesirable effects, and through clinical investigations.

Lastly, it is analyzed the alignment and consistency between the current knowledge and state of the art, the clinical evaluation, the information materials supplied by the manufacturer, and the risk management documentation for the device under evaluation.

Clinical Background, Current Knowledge, State of the Art​

Clinical Background & Current Knowledge​

Overview of Dermatological Conditions and Facial Palsy​

Dermatoses​

Dermatological conditions encompass a wide range of skin-related issues, affecting patients of all ages, ethnicities, and skin types. Symptoms and visual signs can vary significantly, ranging from mild irritations to severe, life-impacting disorders. Common symptoms and visual signs include redness, itching, swelling, and changes in skin texture and color.

Epidemiology​

Dermatological conditions are prevalent worldwide, with acne, eczema, psoriasis, and skin cancer being among the most common disorders.

Aetiology​

The causes of dermatological conditions are diverse, including genetic factors, environmental exposure, allergies, and immune system dysregulation.

Pathophysiology​

The pathophysiology of skin disorders often involves inflammation, abnormal keratinization, and immune system involvement.

Investigation and diagnosis​

Diagnostic modalities for skin conditions include clinical examination, biopsy, dermoscopy, and laboratory tests. Advances in technology have introduced computational tools, enhancing diagnostic accuracy and efficiency.

Facial palsy​

Facial palsy is a condition characterized by sudden, temporary paralysis or weakness of the facial muscles, typically on one side of the face.

Epidemiology​

Facial palsy affects people of all ages, with Bell's palsy being the most common form, impacting 20-30 people per 100,000 annually.

Aetiology​

Causes range from viral infections and nerve damage to systemic diseases and unknown factors.

Pathophysiology​

Facial palsy results from inflammation and swelling of the facial nerve, leading to disrupted nerve signals and muscle weakness.

Investigation and diagnosis​

Diagnosis is primarily clinical, supplemented by electromyography, imaging studies, and, in some cases, blood tests.

Current Treatment Options​

Dermatoses​

Treatment depends on the specific condition and severity, including topical medications, oral medications, light therapy, and surgical options. While many treatments are effective, they may come with side effects, and chronic conditions require long-term management.

Facial palsy​

Treatment aims at improving facial function and may include steroids, antiviral medications, physical therapy, and, rarely, surgery. The recovery varies, with some patients experiencing lasting effects.

Similar devices in the market​

The market offers a variety of computational tools aiming to assist in the diagnostic and evaluative process of dermatological conditions and facial palsy. The devices utilize advanced imaging analysis, machine learning algorithms, and AI-powered diagnostic support to provide accurate and efficient assessments. However, challenges such as cost, required training, and scope limitations exist.

The following table lists devices that are equivalente in terms of technology, but also some devices that may be substitute of the device even if they don't use artificial intelligence. Likewise, some of the devices listed focus on very specific ICD classes, unlike the device.

NameWebsiteCountry of OriginHow to Access and UseFounding YearValue PropositionTargeted Medical ConditionsCE MarkingMedical Device Risk Classification under MDRHardware Requirement
SkinVisionskinvision.comThe NetherlandsDownload in App Store or Browser[Not Found]Skin cancer risk assessment via photos, recommendations for next steps 1Skin CancerYes[Not Found]Mobile devices with certain camera quality
DermEnginedermengine.comCanadaWeb, iOS, Android, tvOS2014 or 2012Imaging, documentation, analysis of skin, hair, nails with AI support 2Dermatology-related conditions[Not Found][Not Found]Compatible with various devices & integration with dermoscopes
Triagetriage.comCanada[Not Found][Not Found]Screening for 588 skin disorders using medical-grade AISkin Disorders[Not Found][Not Found][Not Found]
First Dermfirstderm.comUSAWeb, iOS, Android app2014Online dermatology consultations, not a substitute for doctor's visit but offers medical guidanceSkin Conditions[Not Found][Not Found]Smartphone, optional use of HÜD device for mole scanning
Cureskincureskin.comIndiaWeb, iOS, Android app2016 or 2017AI-powered analysis and expert dermatologist consultations for skin, hair, body, and personal care concernsSkin and Hair Conditions[Not Found][Not Found]Smartphone
MoleMappermolemapper.org[Not Found]iOS App, Android App[Not Found]Mapping, measuring, and monitoring moles to detect potentially harmful changes, aiding in melanoma researchMelanoma Detection[Not Found][Not Found]Smartphone with camera
Eczema Trackereczematracker.com[Not Found]iOS App, Android App2016Analyzing, managing, and controlling eczema; tracking and analyzing triggers; getting advice; maintaining medicine usage record; staying updated on eczema-related newsEczema[Not Found][Not Found]Smartphone
VisualDxvisualdx.comUSA (Rochester, NY)PC, iPad/iPhone, Android Tablet/Phone2001Clinical decision support system aiding in differential diagnosis via peer-reviewed images, intended for medical practitioners including primary care practitionersVarious Medical Conditions[Not Found][Not Found]PC, Tablet, or Smartphone
Legit.Healthlegit.healthSpain (Bilbao)Web Application, iOS, Android2019Aiding in the diagnosis and follow-up tasks of HCPs using computer vision convolutional neural networks for clinical insights on skin lesion images 3Skin DiseasesYesClass IIaAny Device with Internet Access
MoleScope (by Fotofinder)molescope.comCanada (Vancouver, BC)4Attach to specified smartphones or tablets2012Imaging, archiving, and communication of skin conditions5, 6Mole imaging, other skin conditions like acne, eczema, psoriasisYes[Not Found]Specified iOS and Android devices

Literature Search strategy​

A thorough, non-biased, objective and systematic literature assessment of the current state of the art and scientific publications has been performed with the objective to identify possible adverse effects of similar devices and to justify the need for the device to be placed on the market. The literature search strategy is based on two scope areas: preclinical and clinical data evaluation.

Data sources​

The evaluation of data is based on a critical and analytical assessment of the available literature. The different sources utilized are:

The preclinical data evaluation is carried out through:

  • Applicable and/or harmonized standards, according to the Technical File.
  • National and international institutions and medical devices regulatory bodies webs:
    • The Spanish Agency for Medicines and Medical Devices (AEMPS): https://www.aemps.gob.es/
    • The International Medical Device Regulators Forum (IMDRF): http://imdrf.org/safety/safety.asp
    • MedTech Europe: https://www.medtecheurope.org/
    • The European Commission:
      • https://ec.europa.eu/growth/single-market/european-standards/harmonised-standards/medical-devices_en
      • https://ec.europa.eu/info/policies/public-health_en
    • The US Food and Drug Administration (FDA): https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfmaude/search.cfm
    • The Cybersecurity and Infrastructure Security Agency (CISA): https://www.cisa.gov/cybersecurity
    • The European Union Agency for Cybersecurity (ENISA): https://www.enisa.europa.eu/
    • The Notified Body Operations Group (NBOG): https://www.nbog.eu/
  • Other manufacturers' similar devices websites.
  • State of the art publications in renowned journal databases.
  • Information generated by the manufacturer.
Clinical data sources​

For the literature search related to the intended purpose, we have made use of different databases that compile scientific articles and clinical trials published in renowned journals and conferences:

  • PubMed: https://pubmed.ncbi.nlm.nih.gov/
  • Cochrane Library: https://www.cochranelibrary.com/
  • Google Scholar: https://scholar.google.com/

Search filters​

When using the search tool of specific websites or when searching in journal databases, at least the first 20 results are considered for their further evaluation. If the same scientific publication appears under different search characteristics, the repeated source is ignored. The scientific and clinical publications are already sorted out by relevance.

Moreover, in the case of skin lesion analysis, a time filter (last 3 years) is applied in order to ensure that the most recent information is evaluated. Relevant citations referenced in the scientific and clinical literature already selected from the previous sources might also be considered for evaluation.

Research questions and keywords​

The technical documentation of the device as well as the state of the art is used to develop the clinical research questions and keywords.

The search methodology is based on the PICO format. This format has been designed for high-quality clinical research evidence by defining a set of relevant keywords in four different categories (Population, Intervention, Comparison, and Outcome), that are then used to construct the search queries.

Since our research is not limited to clinical research, by also including articles related to state-of-the-art Deep Learning algorithms, paper reviews, and the deployment of Deep Learning solutions, we have modified these categories to also include keywords related to non-clinical terms.

  • P = Patient / Population / Problem: diseases that the software is able to identify or analyze (skin cancer, melanoma, chronic skin conditions).
  • I = Intervention: it describes the main intervention, prognostic factor, or exposure.
  • C = Comparator: it describes the main alternative to compare with the intervention. This can be different devices, tests or placebo. Sometimes the clinical question may not always have a specific comparison.
  • O = Outcomes: diagnosis, identification, analysis, or assessment of certain skin pathologies.

Database search steps​

The defined research keywords are used and the search filters are applied in the clinical data sources as it follows:

Step 1: Use PICO to formulate the search strategy.​
PICO search terms for skin structure-related articles:​
Keywords
Population, Patient, Problem (P)dermatosis, skin cancer, chronic skin conditions, inflammatory skin diseases, malignant skin lesions, pigmented skin lesions, melanoma, basal cell carcinoma, squamous cell carcinoma, atypical nevus, acne, psoriasis, urticaria, atopic dermatitis, onychomycosis, melasma, solar lentigo, dermatofibroma, skin diseases, skin lesions
Intervention (I)clinical image, digital imaging, web application, smartphone, dermatoscopy, camera, CAD, dermatoscope
Comparator (C)artificial intelligence, machine learning, deep learning, computer vision, deep neural networks, convolutional neural networks, metaoptima, automated
Outcome (O)diagnosis, diagnosis support, followup, segmentation, detection, estimation, classification, assessment, severity assessment, improving
PICO search terms for facial palsy-related articles:​
Keywords
Population, Patient, Problem (P)facial palsy, facial paralysis, facial nerve palsy
Intervention (I)photo, video, expression, proportion, clinical image, smartphone, monitoring
Comparator (C)artificial intelligence, machine learning, deep learning, computer vision, deep neural networks, convolutional neural networks, regression, neural network
Outcome (O)prediction, assessment, severity assessment, improving
PICO search strategy​

The search queries used in the PubMed and Cochrane datasets are formed to include at least one of the keywords from each PICO component:

(("P") AND ("#I") AND ("#C") AND ("#O"))

Query example:

(("facial palsy") OR ("facial paralysis") OR ("facial nerve palsy")) AND (("Photo") OR ("video") OR ("expression") OR ("proportion") OR ("clinical image") OR ("smartphone") OR monitoring) AND (("artificial intelligence") OR ("machine learning") OR ("deep learning") OR ("computer vision") OR ("deep neural networks") OR ("convolutional neural networks") OR regression OR neural network) AND (("prediction") OR ("assessment") OR ("severity assessment") OR ("improving"))

Since Google Scholar does not allow such advanced query formations, we follow a similar principle but simplify the queries to include the most relevant keywords and/or different PICO components.

Step 2: Search parameters​

In the article search, when possible we make sure that:

  • The results are sorted by relevance.
  • The specified query matches the text from either the title, abstract, or article keywords.
  • We not only allow exact matches but also word variations in the query keywords.

Step 3: Data screening​

From the compiled articles, we apply an initial set of filters to remove the non-relevant manuscripts:

  • Duplicate articles are filtered out.
  • Non-English articles are filtered out.
  • Articles that are out of the desired scope are filtered out.
  • In the case of skin lesion analysis-related articles, we also filter out articles whose publication journal/conference have a low Impact Factor.
info

A more detailed explanation of the literature search methodology, data screening, and data appraisal can be found in the document R-TF-015-002 Preclinical and clinical evaluation record_2023_001.

Device equivalence​

As described at the corresponding Device equivalence section of the GP-015 Clinical evaluation procedure, the equivalence table from the Guideline MDCG 2020-5 is used to identify the supporting data to demonstrate device equivalence. We place emphasis on the differences between the device and equivalent device rather than the similarities. In line with the MDR 2017/745 requirements, we consider the following characteristics to claim device equivalence:

1. Technical characteristics​

#CharacteristicsLegit.Health PlusLegit.HealthConclusion
1.1Device is of similar designReference: Legit.Health Plus description and specifications 2023_001TF-LEGIT.HEALTH_23_001_MDD_20230404No differences in the characteristic
1.2Used under similar conditions of useReference: Legit.Health Plus description and specifications 2023_001Reference: TF-LEGIT.HEALTH_23_001_MDD_20230404No differences in the characteristic
1.3Similar specifications and properties including software algorithmsReference: Legit.Health Plus description and specifications 2023_001,Reference: TF-LEGIT.HEALTH_23_001_MDD_20230404No differences in the characteristic
1.4Uses similar deployment methods where relevantReference: R-TF-012-006 Legit.Health Plus life cycle plan and report_2023_001Reference: A12.1-QP-22_Plan de desarrollo Software_2.0No differences in the characteristic
1.5Has similar principles of operation and critical performance requirementsReference: Legit.Health Plus description and specifications 2023_001Reference: TF-LEGIT.HEALTH_23_001_MDD_20230404No differences in the characteristic
#Scientific justificationDifference
1.1There would be no clinically significant difference in the safety and clinical performance of the device because the design of the device is the same as the equivalent. The difference is that it's simpler and better, because the only interface is the API, whereas the equivalent device had also an application.No clinically significant difference
1.2There would be no clinically significant difference in the safety and clinical performance of the device because the intended environment is identical in every possible way.No clinically significant difference
1.3There would be no clinically significant difference in the safety and clinical performance of the device because it's the same algorithms. The architechture is more sophisticated and resilient on the new device, and we call them processors instead of algorithms; but they are literally the same neural network.No clinically significant difference
1.4There would be no clinically significant difference in the safety and clinical performance of the device because the deployment methods of the previous device included the ones of the new device. Same explanation as 1.1. Both require access to the internet and are accessible from any device.No clinically significant difference
1.5There would be no clinically significant difference in the safety and clinical performance of the device because the principle of operations are the same, but simpler, because the interface is minimal. The equivalent device had an API and also a web application, whereas the new device is only the API and its means to be integrated.No clinically significant difference

2. Biological characteristics​

Does not apply

Due to the nature of the device being a software-only medical device, this section does not apply. To be clear: there are no materials or substances in contact with human tissues or body fluids. And this is true for both devices. Thus, there is no difference in characteristics.

3. Clinical Characteristics​

#Clinical CharacteristicsLegit.Health PlusLegit.HealthConclusion
3.1Same clinical condition or purpose, including similar severity and stage of diseaseReference: Legit.Health Plus description and specifications 2023_001Reference: TF-LEGIT.HEALTH_23_001_MDD_20230404The characteristic are the same
3.2Similar population, including as regards age, anatomy and physiologyReference: Legit.Health Plus description and specifications 2023_001Reference: TF-LEGIT.HEALTH_23_001_MDD_20230404The characteristic are the same
3.3Same kind of userReference: Legit.Health Plus description and specifications 2023_001Reference: TF-LEGIT.HEALTH_23_001_MDD_20230404The characteristic are the same
3.4Similar relevant critical performance in view of the expected clinical effect for a specific intended purposeReference: R-TF-015-003 Clinical Evaluation Report_2023_001Reference: LEG-001-CER-LEGIT_20201124The characteristic are the same
#Scientific justificationDifference
3.1There would be no clinically significant difference in the safety and clinical performance of the device because they both target visible manifestations of dermatoses. It's worth noting that the indications are declared in a different way, but its simply because the new device expresses them in a more technically accurate way: 'conditions' vs 'ICD classes'.No clinically significant difference
3.2There would be no clinically significant difference in the safety and clinical performance of the device because it's the same population. The device says: "patients presenting visible skin structure abnormalities", whereas the equivalent says "patients who suffer from skin conditions". It's pretty much the same.No clinically significant difference
3.3There would be no clinically significant difference in the safety and clinical performance of the device because it's the same intended user. In both cases, the documentation says: "health care professionals (HCP)". The device is more specific in also mentioning that the technical team of the care provider is a relevant user during integration, but it's a higher level of specification, not a difference.No clinically significant difference
3.4There would be no clinically significant difference in the safety and clinical performance of the device because both show that there is enough evidence to establish the safety and performance of the devices when used in accordance with the IFUNo clinically significant difference

Summary​

It should come as no surprise that the analysis yelded the result of both devices being equivalent, because the device is an evolution of the equivalent device, which constitutes its previous generation.

Read more

The device is the result of focusing all the design efforts in the API, which constitutes the component that performs the clinical tasks also in the previous generation. Thanks to this, the new device is safer, more roboust and simpler than its predecesor. You can find more information about this on the following documents:

  • Legit.Health Plus description and specifications 2023_001
  • R-TF-012-006 Legit.Health Plus life cycle plan and report

Data generated and held by the manufacturer​

Technical and pre-clinical data generated and held by us​

In the clinical study LEGIT.HEALTH_DAO_Derivación_O_2022 conducted at Hospital Universitario de Cruces and four primary care centers, the primary care doctors exhibit low sensitivity, approximately 25% (but have high specificity at 96%). This low sensitivity might be due to the fact that all cases are referred to dermatologists. As a result, the primary care doctor's diagnosis doesn't impact the decision, making it a non-conservative diagnosis. If the diagnosis were made with the intention of either treating it in primary care or referring it to the dermatologist, it's likely that sensitivity would be significantly higher, while specificity would be lower.

This study reveals that approximately 29% of the referrals, even those from teledermatology, involve common and easily diagnosable conditions, with about half of them being related to seborrheic keratosis. Another example of conditions that can be confidently identified and managed without referrals includes skin tags, which the device can reliably confirm, and other entities are unlikely to misdiagnose. In the case of skin tags, the device correctly identifies them as the top1 ICD category with over 40% confidence.

The quality of the images significantly influences the performance of the system. This is a well-established fact in the field because image quality not only impacts the effectiveness of algorithms but also hinders dermatologists from making diagnoses through teledermatology systems. Specifically, poor-quality images of nevi often require an in-person consultation, causing unnecessary delays for specialists.

It's typically complex to calculate precise costs, but we can estimate that devices like ours could have a substantial impact on cost optimization while simultaneously reducing waiting times and expediting urgent cases.

In terms of the waiting list, the analysis assumes that patients could have received treatment earlier, and the appointment delays weren't due to personal reasons but rather a result of the hospital's waiting list.

Another study in Hospital Universitario de Cruces and Hospital Universitario de Basurto shows, again, strong evidence of the capacity of the device to assist healthcare practitioners in their clinical evaluations.

The device demonstrates great malignancy prediction and compelling image recognition capacity in the task of extracting ICD category features, such as such as melanoma, carcinoma, keratoses or nevi, with results similar to internal validation tests. The device achieved an excellent AUC of 0.8769 for the task of malignancy detection. In terms of generating a distribution of possible ICD categories, the device presented compelling top-3 and top-5 accuracies (72.85% and 81.64%), which confirms its utility as a tool to assist healthcare practitioners in a real clinical setting.

A third study held in Hospital Universitario de Torrejón, under the code LEGIT_COVIDX_EVCDAO_2022, provides direct evidence based on the opinion of healthcare professionals that used the tool for a large period. We found out that the device proved to be helpful in their clinical practise. The main endpoint of the study, the Clinical Utility Questionnaire (CUQ), got very positive responses, indicating positive perceptions among specialists, particularly in terms of ease of use and effectiveness in optimizing consultation time according to each patient's needs.

Other secondary endpoints were also questionnaires that gathered relevant information such as the data utility and ease of use of the medical device. The Data Utility Questionnaire affirms unanimous agreement on the device's intended usefulness. System Usability Scale results showed high levels of user satisfaction and ease of navigation. Patient satisfaction scores reflected a positive overall experience with the tool. Importantly, no adverse events or reactions were observed, highlighting its favorable safety profile.

The device proves highly effective, safe, and user-friendly for managing dermatosis. Positive feedback from specialists and patients underscores its potential as a valuable clinical tool. The device exhibits significant clinical relevance for its intended use, offering objective clinical data for dermatosis evaluation.

Overall, the device proves to be a valuable support tool for healthcare practitioners and organizations in their clinical decision-making process.

Clinical data generated and held​

We have disseminated our findings through four distinct articles, all accessible to the scientific community in reputable journals like JAAD and JID Innovations. These articles are:

  • Automatic SCOring of Atopic Dermatitis using Deep Learning: A Pilot Study
  • Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A Novel Tool for Assessing Hidradenitis Suppurativa Severity with Artificial Intelligence
  • Dermatology Image Quality Assessment (DIQA): Utilizing Artificial Intelligence to Ensure the Clinical Applicability of Remote Consultation and Clinical Trial Images
  • Automatic Urticaria Activity Score (AUAS): Deep Learning-Based Automatic Hive Counting for Urticaria Severity Evaluation

These articles collectively demonstrate the performance of the medical device in the extraction of quantifiable data regarding the count, extent, and intensity of clinical signs, as well as evaluating the quality and clinical relevance of dermatological images.

In addition to our published literature, we have conducted additional and extensive analyses to assess the performance of these algorithms in extracting quantifiable clinical sign data and the distribution of potential ICD categories. Our Post-Market Clinical Follow-up activities have consistently shown that the device's performance aligns with or even surpasses the initial test results in specific scenarios.

In real-world applications, the device achieved results exceeding the specified metrics outlined in REQ_004_The user receives an interpretative distribution representation of possible ICD categories represented in the pixels of the image. Healthcare organizations that used the interpretative distribution representation of possible ICD categories for their clinical decision-making process as a main feature of the medical device, achieved impressive results, with 70% top1, 85% top3, and 92% top5 success rates.

When it comes to malignancy detection, the AUC (Area Under the Curve) obtained directly from user data is outstanding, with a value of 0.96. Even when focusing exclusively on specific ICD category groups such as the pigmented and tumoral dermatosis, a category where the proportion of malignant cases is higher than the overall average, the AUC remains excellent, consistently exceeding 0.90.

Clinical Data​

Clinical Data From Literature​

We have compiled all the clinical data from literature at the T-015-002 Pre-clinical and clinical evaluation record.

In accordance with the T-015-001 Clinical Evaluation Plan, the acceptance or rejection of each piece of information is determined through the application of the following weighted-based system:

Data characteristicsMethodological quality QRelevance, RContribution, CWeighted value, W (W=Q+R+C)Appraisal dataNotes
Very relevant information in relation with the product and its intended use303030W ≥ 70AcceptedPivotal data
Relevant information in relation with the product and its intended use20202030 < W < 70AcceptedOther data
Little relevant information in relation with the product and its intended use101010W ≤ 30RejectedNo contribution, rejected

As it is described at the T-015-001 Clinical Evaluation Plan, preclinical data is accepted when it provides comparable technical information of equivalence/similar device on the market or information about similar accessories. On the other hand, preclinical data is rejected when the information is incomplete, insufficient, or irrelevant.

Scientific publications are accepted when the study design is adequate, and when they provide information that clarifies whether the use of the device is necessary, safe, and advantageous over other alternatives on the market. That is, when the intended use, the target patient population, or the target user group matches the device. On the other hand, scientific publications are rejected when their content do not discuss the topic of interest, when the conclusions are inconsistent or there are misinterpretations, when there is a lack of information on basic aspects, or when the information is inaccessible or repetitive.

Appraisal of clinical data​

Skin structures analysis​

In the literature review for skin analysis methods, 188 articles were compiled, of which 81 articles passed the Data Screening and 107 were discarded. These 81 articles that passed the Data Screening were reviewed thoroughly to assess their Quality, Relevance, and Contribution. As a result:

  • 31 articles have been evaluated as highly relevant.
  • 25 articles have been evaluated as relevant.
  • 25 articles have been evaluated as non-relevant.

In this review, we identified different kinds of skin analysis methods that are intended for different use cases:

  • Most commonly, these solutions are designed for skin-related image diagnosis (IDs 019, 020, 030, 034, 042, 044, 045, 047, 053, 058, 061, 063, 067, 068, 072, 078, 079, 080, 083, 085, 092, 096, 119, 122, 127, 128, 129, 132, 166), usually being able to detect 2 to 10 different skin diseases but in particular cases up to 174 different lesion categories. Of special relevance, certain solutions include novel techniques to increase the diagnosis performance. IDs 092 and 128 jointly process image and metadata information, ID 020 combines image and lesion segmentation information, ID 080 attaches a special device to a smartphone camera to improve the image quality and ease its processing, and IDs 042 and 132 include different tricks such as knowledge distillation, cost-sensitive learning, soft targets, cumulative learning or specific data augmentations to ease the learning with small and unbalanced datasets.
  • Differently, other methods are designed for lesion segmentation with pixel-precision or for the severity assessment of different pathologies. In the case of lesion segmentation (IDs 016, 025, 027, 031, 037, 064, 076, 122), we find interesting novelties such as ID 016 that includes the use of special pre and post-processing steps, and ID 064 that proposes a new architecture designed with fewer parameters to perform more efficiently. And, as for the case of lesion severity assessment, we find some methods designed for the psoriasis (IDs 009) or acne (IDs 021, 035, 093) evaluation.
  • Besides the design and development of these image analysis methods, we also find some other articles that include information related to their deployment and integration in phone apps and/or the cloud (IDs 019, 021, 035, 061, 087). Of special relevance, ID 021 proposes the pruning and feature-based knowledge distillation of the trained models to improve their efficiency in end-devices.
  • Interestingly, we find also review articles that summarize the state-of-the-art of skin lesion analysis methods (IDs 001, 007, 009, 014, 015, 019, 113, 116, 120, 121, 146, 187, 188), including important tips for their design, deployment, and analysis of the challenges they face.
  • Finally, other research articles also provide information related to the evaluation of these methods in clinical trials (IDs 019, 021, 030, 053, 081, 085, 140, 143, 184), providing meaningful information related to the performance of these methods in real environments and the benefits they offer to health professionals.

Facial palsy analysis​

Regarding the literature review for facial palsy analysis methods, 46 articles were compiled, of which 31 articles passed the Data Screening and 15 were discarded. These 31 articles that passed the Data Screening were reviewed thoroughly to assess their Quality, Relevance, and Contribution. As a result:

  • 15 articles have been evaluated as highly relevant.
  • 10 articles have been evaluated as relevant.
  • 6 articles have been evaluated as non-relevant.

In this review, we identified different articles that develop solutions intended for different use cases:

  • Although we find some solutions focused on diagnosing facial palsy (ID 005) or predicting the probability of incurring it after specific surgeries (IDs 006, 009, 010), most works are based on the facial palsy severity assessment. To this end, these methods mainly rely on the analysis of facial landmarks (IDs 001, 003, 015, 016, 021, 022, 038), but we also find some other works that use manual labelling (ID 007), video or image processing (IDs 016, 018, 020), or automated emotion analysis (IDs 002, 013, 017, 019). For this severity assessment, some articles (IDs 003, 022, 046) also propose the creation of specific datasets to ease the analysis tasks.
  • Interestingly, we find some article reviews (IDs 011, 012, 023, 024, 026) that describe the state-of-the-art of intelligent facial palsy-related methods.
  • Finally, many of these solutions (IDs 001, 003, 005, 006, 007, 009, 010, 013, 014, 016, 017, 018, 019, 021) are involved in clinical trials to gather patient information for the method evaluation, providing meaningful information related to the performance of these methods in real environments and the benefits they offer to health professionals.

Clinical Data from Clinical Study Databases​

The table shows the results obtained on the literature search in relation to similar devices used in the skin structures field. Regarding the clinical data found in relation to facial palsy, they use real patient data to train some model, but there are no specific metrics on whether they improve outcomes over doctors because in this case the tasks they do are different.

Name of similar deviceResults discussedReferences used to get the results
FotofinderAlthough their Moleanalyzer Pro tool just focused on moles, their findings suggest that dermatologists may improve their performance when they cooperate with the CNN (Convolutional Neural Network) and that a broader application of this human with machine approach could be beneficial for dermatologists and patients2804568
7-class skin disease recognitionThis AI has been trained on skin-related data collected in hospitals from the Southwest of Ethiopia, Eastern Amhara, and Afar region. Final device works with an accuracy, precision, and sensitivity of above 97%, showing high safety and performance to be used as an assistive tool.ID 019
Acne severity assessment appThe software developed shows a great performance for the acne severity assessment, being able to count and classify with high precision the different acne lesions. The performance of this app surpasses the General Practitioners' and gets close to the more experienced dermatologists. However, since the app could be biased towards the Chinese-like population, data from other regions should be included in the learning system.ID 021
10-class cutaneous tumor recognitionThis study shows how the use of this AI can assist dermatologists in increasing their lesion analysis performance. In particular, this boost of performance is bigger for the dermatologists with less experienceID 030
mHealth app (CE-marked)40 skin diseases recognition AI trained in images of skin of colour from India. The app reflects an top-1 accuracy of 75% in clinical trials, 89% of top-3 accuracy, and 0.90 AU, showing its viability as a clinical decision support tool.
A posterior independent study revealed that the app presents a sensitivity and specificity that surpasses the one from General Practitioners and gets close to the dermatologists' one. The performance also decreases depending on the phone device used, so the image quality should be taken into account.		ID 053
ID 081
174-class skin disease recognitionThis study reveals that this AI model gets comparable top-1 accuracy (47.6%) to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%), showing promising capabilities as an assistance for skin lesion recognitionID 085
Two applications able to recognize 47 lesion categories, that fulfill the CE-criteria, and registered as medical product at the Austrian Federal Office for Safety in Health CareA clinical trial in Austria reveals that these two apps present high sensitivity and specificity (94-96%), probing its performance and suitability for skin lesion analysis assistance.ID 140
Review of 272 clinical studies that include different AI solutions to facilitate the early diagnosis of skin cancers, especially in primary and community care settings.This study reveals an average high accuracy for the recognition of melanoma (89%), squamous cell carcinoma (85%), basal cell carcinoma (87%), and malignancy estimation (88%). Although these numbers show the potential benefits of AI for skin lesion analysis (especially in primary care), some studies present different concerns related to the size, variability, and source of the studied population.ID 143
Study of the benefits when using AI assistance for skin image analysisThe study involves 20 primary care physicians and 20 nurse practitioners with different levels of experience. When assisted by the AI, these practitioners increased their diagnosis agreement, demanded fewer biopsies and referrals, and increased their confidence.ID 184

Clinical Data From Adverse Event Databases​

We consulted the following sanitary alert databases specified at the table looking for incidents or alerts by using similar devices or methodologies as ours. We consulted in these databases the following aspects:

  • The similar devices described: Dermengine, Fotofinder hadyscope pro app, Skinscreener, Skinvision and Triage.
  • The following keywords: artificial intelligence, dermatology, deep learning, medical imaging, computer vision, as we had specified at the R-TF-007-001 PMS plan 2023_001.
Source of informationLinkResultsAnalysis
FDA website MAUDE - manufacturer and User Facility Device Experience Searchable databaseaccessdata.fda.gov0
FDA website Medical Device Recallsaccessdata.fda.gov0
German Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM)bfarm.de25Results obtained using the keywords "medical imaging". The incidences found have no relation to our device
Swissmedic Swiss Competent Authorityswissmedic.ch71Results obtained using the keywords "medical imaging". The incidences found have no relation to our device, they are mainly related to X-ray and IVD devices
AEMS Vigilancia de productos sanitariosalertasps.aemps.es0
MHRA Adverse events reportinggov.uk15Results obtained using the keywords "artificial intelligence" and "deep learning". Although one result is related to a PACS, this one and the other incidences are not related to our device
Ministero della Salute - Avvisi di sicurezza sui dispositivi medicisalute.gov.it1Result obtained using the keyword "dermatology", but the incidence was related to cosmetics.

Regarding the similar devices, only the Triage search yielded results, that are not included within the table as they are related to cardiac devices.

Analysis of the Clinical Data​

Requirement on safety​

According to the T-015-001 Clinical Evaluation Plan, we analyze the data accepted from the appraisal in order to reach conclusions about the safety and clinical performance of the device including its clinical benefits and possible adverse events, following stipulations from Appendix A7.1 of MEDDEV 2.7.1. rev.4..

Special design features and safety concerns​

The device leverages advanced computer vision algorithms to analyze images of the skin and its various structures. There are no medicinal, human, or animal components present in the device, mitigating concerns related to biocompatibility and potential adverse reactions to biological materials. However, the risk management documentation identified potential safety concerns related to the accuracy and reliability of the device, particularly in cases of poor image quality or uncommon skin conditions.

Addressing risks from risk management documentation and literature​

All risks identified in the risk management documentation have undergone thorough evaluation from both a technical and clinical perspective. This includes ensuring the device's performance remains robust across diverse patient populations, skin types, and conditions. Clinical precautions for the reduction of risks and the management of potential adverse events have been outlined, with particular attention paid to ensuring healthcare practitioners are aware of the device's role as a supplementary tool in clinical decision-making, rather than a standalone diagnostic solution.

Training and user precautions​

Given the sophisticated nature of the technology, there is a requisite need for user training to ensure the device is used correctly and effectively. This training will cover proper image capture techniques, interpretation of the device's output, and understanding the device's limitations. The Intended users, healthcare organizations, and their stakeholders, including healthcare practitioners, have been deemed adequate, given their professional background and experience in clinical settings. All training requirements and user precautions have been explicitly described in the Instructions for Use (IFU) accompanying the device.

Consistency with current knowledge and state of the art​

There is full consistency between the current knowledge/state of the art in computer vision applications in dermatology, the clinical data available for the device, the information materials provided by the manufacturer, and the device's risk management documentation. The clinical benefits, as well as any potential risks associated with the device's use, are clearly communicated to the end-users, ensuring transparency and promoting safe usage.

Cybersecurity​

Given that the device is a software-based tool, cybersecurity is a paramount concern. The device complies with relevant standards and best practices to ensure data integrity, protect patient information, and guard against unauthorized access or potential cyber threats. Regular updates and security patches are part of the device's maintenance plan to address any emerging vulnerabilities promptly and ensure the device's ongoing safety and performance.

Requirement on acceptable Benefit/Risk​

In accordance with the Appendix A7.4 of MEDDEV 2.7.1. rev.4., this section provides a thorough evaluation of the device's benefit/risk profile, ensuring a high level of health and safety protection for the patients. The device's extensive market exposure, the substantial benefits it offers, and the low probability of undesirable side-effects contribute to its positive benefit/risk ratio, ensuring its acceptability across various medical conditions and target populations.

Total experience with the device​
Clinical investigations and PMCF​

The device has undergone extensive clinical investigations and Post-Market Clinical Follow-up (PMCF), involving a diverse range of patients across various demographics and skin conditions. The investigations have provided valuable data on the device's performance, accuracy, and reliability in real-world settings.

User experience and market exposure​

The device has been available in the market for a substantial period, accumulating a significant user base among healthcare professionals. The estimated number of patients exposed to the device exceeds a thousand, with a follow-up duration ranging from immediate results interpretation to longitudinal studies spanning several months.

Nature and extent of benefits and risks​
Benefits to the patients​

The device offers numerous benefits, including quick and accurate assessments of skin conditions, aiding in early detection and timely intervention. The nature of the benefits is substantial, contributing to improved patient outcomes and enhanced efficiency in clinical workflows.

  • Extent/Severity: The benefits are significant, especially for patients requiring immediate attention and those in remote or underserved areas.
  • Probability/Frequency: The probability of patients experiencing the benefits is high, given the device's proven accuracy and reliability.
  • Duration: The benefits are long-lasting, with the potential to influence patient outcomes positively over an extended period.
Undesirable side-effects and other Rrisks​
  • Nature: The primary risk is associated with the potential for misdiagnosis or misinterpretation of the device's results.
  • Extent/Severity: The severity of the risks is mitigated by the device's design to be used as a supplementary tool, not a standalone diagnostic device.
  • Probability/Frequency: The probability of occurrence is low, thanks to rigorous testing, validations, and continuous improvements.
  • Duration: Any potential misdiagnosis can be promptly addressed through follow-up consultations, minimizing the duration of any adverse effects.
Evaluation of the benefit/risk profile​

For each aspect of the intended purpose, the benefit/risk profile, including its uncertainties or unanswered questions, has been evaluated and is found to be compatible with a high level of protection of health and safety. The device's design, functionality, and the support it provides to healthcare professionals have been justified through clinical data, user feedback, and continuous monitoring.

Acceptability of benefits and risks across medical conditions and target populations​
  • All Medical Conditions and Target Populations: The device's benefits and risks have been evaluated across all medical conditions and target populations covered by its intended purpose.
  • Evaluation for Specific Populations: Special considerations have been taken into account for vulnerable populations, ensuring that the benefit/risk profile remains favorable.
  • Limitations: While the device is suitable for a broad range of applications, any limitations have been clearly identified and communicated to the end-users, ensuring informed and safe use.

Requirement on performance​

Achieving Intended Performance during Normal Conditions of Use​

The device has undergone rigorous testing and validation to ensure that it performs reliably under normal conditions of use. Various real-world scenarios and clinical settings have been simulated to test the device's robustness, accuracy, and reliability. The testing has confirmed that the device achieves its intended performances consistently, aiding healthcare professionals in diagnosing and managing skin conditions effectively.

Support by Sufficient Clinical Evidence​

Extensive clinical investigations and data analysis have been carried out to ensure that the device's performances are well-supported by clinical evidence. This encompasses a thorough evaluation of the device's valid clinical association, technical performance, and clinical performance.

Valid Clinical Association​
  • Literature Search: A comprehensive literature search has been conducted to establish the correlation between the device's output and the targeted physiological state or clinical condition defined in its intended purpose.
  • Proof of Concept: Various proof of concept studies have been undertaken to validate the clinical association of the device's algorithms and outputs.
  • Equivalence: The device has been benchmarked against existing solutions and standards, demonstrating equivalent or superior performance.
Technical Performance​
  • Verification and Validation Activities: The device has undergone extensive verification and validation activities to ensure its technical soundness.
  • Accuracy, Reliability, and Precision: The device consistently generates accurate, reliable, and precise outputs from the input data.
  • Positive Predictive Value (PPV) and Negative Predictive Value (NPV): The device exhibits high PPV and NPV, ensuring that its diagnoses and recommendations are trustworthy and reliable.
Clinical Performance​
  • Clinically Relevant Output: The device has proven its ability to yield clinically relevant outputs that align with its intended purpose.
  • Clinical Papers and Studies: Numerous clinical papers and studies support the device's clinical performance, showcasing its benefits and effectiveness in various clinical settings.
  • Equivalence and Benefits: The device has demonstrated equivalence to, or outperformance of, existing solutions, offering tangible benefits to both patients and healthcare providers.
Conclusion​

In summation, the device achieves its intended performances during normal conditions of use and these performances are substantiated by clinical evidence. The valid clinical association, exceptional technical performance, and proven clinical performance of the device collectively affirm its efficacy, reliability, and value in clinical practice.

Requirement on acceptability of undesirable side-effects​

Ensuring the safety of the patients is paramount, and part of this process involves a meticulous assessment of any potential undesirable side-effects associated with the use of the device. This section is dedicated to evaluating the acceptability of these side-effects, taking into account the available data and any limitations or uncertainties that might be present.

Evaluation of available data​
  • Sufficient Amount and Quality: We have amassed a comprehensive dataset through clinical trials, post-market clinical follow-up (PMCF), and real-world usage. This dataset is deemed to be of sufficient amount and quality, providing a robust basis for the detection of any potential undesirable side-effects.
  • Frequency of Side-Effects: The data has been meticulously analyzed to determine the frequency of any undesirable side-effects. This analysis has shown that there are no side-effects, falling well within acceptable safety margins.
  • Limitations and Gaps: Despite the comprehensive nature of our dataset, we acknowledge that no dataset is without its limitations. Potential gaps in the data could include the underreporting of minor side-effects or the lack of long-term follow-up data for certain user demographics. We are committed to continuously improving our data collection and analysis methods to address these limitations.
  • Uncertainties and Unanswered Questions: While our current dataset provides a solid foundation for assessing the safety of the device, there may still be uncertainties or unanswered questions. For instance, the long-term effects of continuous usage of the device remains an area for future research and investigation.
Acceptability of undesirable side-effects​
  • Risk-Benefit Analysis: The extremely low frequency of undesirable side-effects, coupled with the significant clinical benefits offered by the device, results in a highly favorable risk-benefit ratio. The benefits in terms of improved diagnostic accuracy, enhanced patient outcomes, and increased efficiency in clinical workflows far outweigh the potential risks.
  • Justifications for Acceptability: The acceptability of the undesirable side-effects is justified by the rigorous testing, continuous monitoring, and ongoing improvements made to the device. The risk management processes, along with the transparent communication of any potential risks to the users, further supports the acceptability of these side-effects.
Conclusion​

Through rigorous data analysis, continuous improvement processes, and a commitment to patient safety, we have ensure that any potential risks are minimized and clearly communicated, resulting in a product that meets the highest standards of safety and efficacy.

Post-Market Activities​

As we embark on the journey of continuous monitoring and evaluation of the device post its market release, this section aims to summarize the various Post-Market Surveillance (PMS) and Post-Market Clinical Follow-up (PMCF) activities that are either planned or already underway. We will reference our existing documents and records to provide a comprehensive overview.

Reference to Key Documents and Records​

GP-007 Post-market Surveillance​

This document provides a detailed guideline on the procedures and methodologies employed in the post-market surveillance of the device. It encompasses a variety of activities aimed at collecting and analyzing data related to the device's performance and safety post-market release.

T-007-001 Post-market Surveillance (PMS) Plan​

This plan outlines the specific strategies and activities that will be implemented for the continuous monitoring of the device's performance and safety in a real-world setting. It serves as a roadmap, ensuring that all necessary measures are in place to detect and address any potential issues promptly.

T-007-002 Post-market Clinical Follow-up (PMCF) Plan​

The PMCF plan is focused on the ongoing collection of clinical data post-market release. This data is crucial for confirming the clinical safety and performance of the device, and for ensuring that the benefit/risk ratio remains favorable throughout the device's lifecycle.

T-007-003 Periodic Safety Update Report (PSUR)​

This report provides a periodic update on the safety aspects of the device, including any new risks identified, the results of risk-benefit analyses, and the measures taken to mitigate risks. It plays a vital role in ensuring the ongoing safety of the device.

T-007-004 PMS Evaluation Report​

This report provides a comprehensive evaluation of the data collected through post-market surveillance activities. It assesses whether the device continues to meet its intended purpose and safety requirements and whether any adjustments or improvements are necessary.

T-007-005 PMCF Evaluation Report​

Similar to the PMS evaluation report, the PMCF evaluation report analyzes the clinical data collected post-market release. It ensures that the device continues to provide clinical benefits and maintains its safety profile in a real-world setting.

Data Tracking for Clinical Benefits and Safety Verification​

Although this is our initial certification and the reports may currently be somewhat shallow, we have established robust systems for tracking a wide array of data to verify our claims of clinical benefits and safety. This includes, but is not limited to:

  • Incident and adverse event reporting
  • User feedback and complaints
  • Clinical outcomes and performance data
  • Device reliability and durability data
  • Comparative analyses with similar products in the market

Through these PMS and PMCF activities, and by meticulously following our established plans and guidelines, we aim to uphold the highest standards of safety and performance for the "Legit.Health Plus" device, ensuring that it continues to deliver on its promises of enhancing patient care in dermatology.

GAP analysis​

In this comprehensive GAP analysis, we critically evaluate whether the assessed data from various sources, including clinical trials, market experience, literature reviews, and state-of-the-art (SOTA) comparisons, have collectively validated and evidenced the clinical safety, performance, and benefit/risk ratio of the device. We also delve into aspects that might not have been adequately covered and outline Post-Market Clinical Follow-Up (PMCF) activities planned for continuous product evaluation.

Validation and Evidence of Clinical Safety and Performance​

Clinical Safety​

The extensive data collected from various clinical studies and real-world usage have demonstrated the device's high safety standards. There have been no significant adverse events reported that can be directly attributed to the device.

However, there is a minor gap in long-term safety data, particularly in the context of prolonged continuous use, due to the limited time that the previous generation has been in the market.

Clinical Performance​

The device has shown excellent performance in line with its intended use, providing accurate and reliable diagnostics in dermatology. Nonetheless, there is a need for additional data to further validate its performance across diverse demographic groups.

Benefit/Risk Ratio​

The benefit/risk ratio has been established as favorable, with the device's advantages significantly outweighing its potential risks. The device enhances diagnostic accuracy, reduces time-to-treatment, and improves patient outcomes. However, a more exhaustive analysis of long-term benefits compared to risks is required to fortify this conclusion.

Relation with Market Experience and Literature​

Market Experience​

For devices previously available in the market, user feedback and post-market surveillance data have been integral in affirming the device's safety and efficacy. There is a consistent positive trend in user satisfaction and performance reliability, although there is a recognized need for ongoing vigilance and data collection.

Literature and State-of-the-Art Comparison​

The literature review and comparison with SOTA technologies affirm the device's standing in the current dermatological landscape. It aligns well with, and in certain aspects, surpasses existing solutions. However, continuous monitoring of emerging technologies and practices is essential to maintain its competitive edge and relevance.

PMCF Activities​

To address the identified gaps and ensure the prolonged safety and efficacy of the device, a robust PMCF plan has been formulated. This includes:

  • Long-term safety and performance studies: Conducting additional studies to gather more long-term safety and performance data.
  • Diverse population analysis: Extending research to include a wider demographic to ensure the device's efficacy across varied populations.
  • User feedback and post-market surveillance: Continuing to collect and analyze user feedback and post-market data to identify areas for improvement and ensure ongoing compliance with safety and performance standards.
  • Technology and practice ponitoring: Keeping abreast of advancements in dermatological practices and technologies to ensure the device remains up-to-date and competitive.

In conclusion, while the device has demonstrated its efficacy and safety, this GAP analysis highlights areas requiring further attention and monitoring. The planned PMCF activities are poised to address these gaps, ensuring continuous validation of the device's clinical benefits and the maintenance of its favorable benefit/risk ratio.

Conclusions​

The comprehensive clinical evaluation of our medical device software has been conducted in accordance with the relevant guidelines and standards, aiming to validate its safety, performance, and clinical benefits. This section encapsulates the key conclusions derived from the clinical evaluation, ensuring that the device fulfills its intended goals and adheres to the General Safety and Performance Requirements (GSPR).

Validation of Safety and Performance​

  • Safety: The clinical data robustly demonstrates that the device is safe for use. The risks associated with the device have been identified, analyzed, and mitigated to an acceptable level. The device's performance remains consistent and reliable, ensuring patient safety.
  • Performance: The device achieves its intended performance under normal conditions of use. The clinical evidence supporting the device's performance is substantial, confirming that it operates accurately, reliably, and efficiently.
  • Clinical Benefits: The device offers significant clinical benefits, enhancing diagnostic accuracy, improving patient outcomes, and optimizing clinical workflows. The benefits far outweigh any potential risks, establishing a favorable benefit-risk ratio.

Validation of User Information​

  • Labelling and Instructions for Use (IFU): The information provided to the users, including labelling and IFU, has been thoroughly validated. It ensures clear, accurate, and comprehensible guidance, facilitating safe and effective use of the device.

Conclusion Table for GSPR​

General requirementsApplicable?Related hazard or hazardous situation risksConclusion that validates risk management and mitigation
GSPR 1: The product shall achieve the intended performance and be suitable for its intended purpose. The product shall be safe and effective and shall not compromise the clinical condition or the safety of patients.TRUE5, 6, 7, 9, 20The device has undergone rigorous testing, demonstrating consistent performance and safety. The risks of malfunction and inaccurate results have been mitigated through design and validation processes.
GSPR 2: Eliminate or reduce risks as far as possible without adversely affecting the benefit-risk ratio.TRUE21Risks have been reduced as far as possible without adversely affecting the benefit-risk ratio, ensuring a high level of protection for patient health and safety.
GSPR 3: Establish, implement, document and maintain a risk management system. Establish and document a risk management plan, identify and analyze the known and foreseeable hazards, estimate and evaluate the risks, eliminate or control the evaluated risks, and evaluate the impact of information from the production phase and post-market surveillance system and amend control measures if necessary.TRUE21, 27A comprehensive risk management system is in place, with all risks being adequately identified, analyzed, and managed. The system is continuously updated based on post-market surveillance.
GSPR 4: Manage risks so that the residual risk associated with each hazard as well as the overall residual risk is judged acceptable. eliminate or reduce risks as far as possible through safe design and manufacture, take protection measures in relation to risks that cannot be eliminated, provide information for safety and, when appropriate, training to users. Inform users of any residual risks.TRUE5, 6, 9, 21, 26, 28, 29, 30All known risks have been managed to ensure that residual risks are at an acceptable level. Protection measures and user information are in place for risks that cannot be eliminated.
GSPR 5: Eliminate or reduce risks related to use error.TRUE1, 2, 3, 4, 8, 16, 17, 18, 19, 31, 36Risks related to use error have been minimized through user-friendly design, comprehensive user testing, and clear user instructions.
GSPR 6: The characteristics and performance shall not be adversely affected during the lifetime of the product.TRUE40, 48, 49, 50, 51Due to the nature of the device, it's characteristics and performance remain stable throughout its intended lifetime.
GSPR 7: The characteristics and performance shall not be adversely affected during transport and storage.FALSE
GSPR 8: All known and foreseeable risks shall be acceptable when weighed against the evaluated benefits to the patient and/or user.TRUE9, 26, 41, 54All known and foreseeable risks have been assessed and deemed acceptable when weighed against the evaluated benefits to the patient and/or user.
GSPR 9: For the devices referred to in Annex XVI, the general safety requirements set out in Sections 1 and 8 shall be understood.FALSE
GSPR 10: Chemical, physical and biological properties.FALSE
GSPR 11: Infection and microbial contamination.FALSE
GSPR 12: Products incorporating a medicinal product absorbed by the human body.FALSE
GSPR 13: Products incorporating materials of biological origin.FALSE
GSPR 14: Construction of products and interaction with their environment.TRUE1, 2, 4, 11, 13, 16, 19, 23, 25, 31, 36The device is robustly constructed to withstand interactions with its intended environment, ensuring consistent performance and safety.
GSPR 15: Products with a diagnostic or measuring function.TRUE34, 35, 37, 38The device provides accurate and reliable measurements, as confirmed through validation testing and clinical trials.
GSPR 16: Protection against radiation.FALSE
GSPR 17: Electronic programmable systems.TRUE25, 26, 29, 34, 35, 37, 38The electronic programmable systems have been rigorously tested and validated to prevent software malfunctions, ensuring the device's reliable performance.
GSPR 18: Active products and products connected to them.TRUE5, 6, 7, 11, 12, 13, 14, 15, 26, 29, 40The device has been designed to ensure users can easily connect to it, which was validated with the usability tests performed.
GSPR 19: Particular requirements for active implantable products.FALSE
GSPR 20: Protection against mechanical and thermal risks.FALSE
GSPR 21: Protection against risks posed by products supplying energy or substances.FALSE
GSPR 22: Protection against risks posed by medical products intended by the manufacturer for use by lay persons.FALSE
GSPR 23: General requirements regarding the information supplied by the manufacturer. Information on the label. Information on the packaging which maintains the sterile condition of a device (‘sterile packaging'). Information in the instructions for use.TRUE1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 18, 19, 22, 23, 24, 25, 28, 29, 30, 36, 41, 43, 44, 45, 46, 48, 49, 51, 53, 54Comprehensive information, including device label and instructions for use, has been validated and provided to users, ensuring safe and effective device use.

In conclusion, the clinical evaluation and risk assessment of the device affirm its safety, performance, and clinical benefits. The GSPR requirements have been meticulously addressed, with processes in place to manage risks and ensure the highest standards of safety for patients and users. The device stands as a reliable, effective, and safe tool, contributing positively to patient care and clinical outcomes.

Date of the Next Clinical Evaluation​

Considering the device risk analysis as well as the establishment of the device within the current state of the art, a new preclinical and clinical evaluation of the available literature will be performed every year using the same weighted-based system. Occasionally, it will be actively updated when we receive new information from PMS and PMCF that has the potential to change the current evaluation of the benefit/risk profile and the clinical performance and clinical safety of the device.

Qualification of the Responsible Evaluators​

Taig Mac Carthy​

  • Job position: JD-003 Design and Development Manager
  • Education: with a specialization in Strategic Management and Innovation from Copenhagen Business School, he has a foundational understanding of business practices essential in product development. His knowledge in quality management systems is well-established, having completed ISO 13485, ISO 9001:2015, and ISO 27001 Lead Auditor certifications from Bureau Veritas Group. These certifications underscore his ability to maintain high-quality standards in device manufacturing. Additionally, his training in ICH Good Clinical Practice and as an Equal Opportunity Agent, alongside courses in Python, Data Science, and Graphic Design, provide a diverse skill set applicable to his current role. His academic journey also includes a degree from the University of the Basque Country.
  • Experience with the product/process/technology: solid background in both the medical and entrepreneurial fields. He has contributed to four scientific publications in computer vision applied to medicine, showcasing his expertise in areas directly relevant to medical device development. His involvement from the inception of the company, given his position as co-founder, has afforded him comprehensive knowledge of the device's development journey. His six years as a front-end software developer and the founding of three companies demonstrate his technical skills and entrepreneurial mindset. Additionally, his authorship of two business management books indicates his grasp on business operations, all of which collectively support his capacity to lead in design and development.
  • Valuation: QUALIFIED
  • Date: 02/07/2023
  • Approved by: Alfonso Medela (JD-005 Technical Manager & PRRC)

Alfonso Medela​

  • Job position: JD-005 Technical Manager & Person Responsible for Regulatory Compliance (PRRC)
  • Education: He holds a degree in physics from the University of the Basque Country. In addition, he completed his training with a MSc. in Physics at the University of Groningen and a MSc. in Big Data and intelligence. University of Groningen and another Master in Big Data and Business Intelligence at the University of Deusto.
  • Experience with the product/process/technology: expert in computer vision, machine learning and artificial intelligence with more than 5 years of experience in the development of projects with medical approaches. His experience includes his time at Tecnalia Research & Innovation where he worked as a data scientist focused on Deep Learning algorithms in the area of Computer Vision. He has written 7 papers on machine learning and image recognition, he also teaches workshops and courses on machine learning and deep learning. At the European level, he is one of the few experts on the few-shot learning methodology in the field of artificial intelligence.
  • Training in risk management and other applicable: ISO 13485 and Medical Devices regulatory.
  • Valuation: QUALIFIED
  • Date: 02/07/2023
  • Approved by: Andy Aguilar (JD-001 General Manager)

María Diez​

  • Job position: JD-004 Quality manager & Person Responsible for Regulatory Compliance (PRRC)
  • Education: María studied Biology at the Complutense University of Madrid. In addition, she holds a PhD on Biochemistry and Molecular Biology by the same University.
  • Experience with the product/process/technology: With more than 7 years on Quality and Regulatory experience, María started developing her abilities implementing a Quality Management system based on ISO 15189, CLIA and Spanish sanitary regulations (specific for medical laboratories), clinical studies and in vitro Software as medical device. On her last work experience she developed and integrated QMS combining the ISO 9001, ISO 13485, ISO 15189 and ISO 27001 regulations with the requirements established at the 2017/746 European in vitro medical device regulations, again for a Software as medical device.
  • Training in risk management and other applicable: ISO 14971, ISO 13485, ISO 9001, ISO 27001, 2017/745 Medical Device Regulations.
  • Valuation: QUALIFIED
  • Date: 02/07/2023
  • Approved by: Alfonso Medela (JD-005 Technical Manager & PRRC)

Alberto Sabater​

  • Job position: JD-009 Medical data scientist
  • Education: Alberto studied Computer Engineering (data science specialization) at the University of Zaragoza, where he also obtained his PhD in Deep Learning and Computer Vision.
  • Experience with the product/process/technology: PhD studies on Efficient scene understanding from video data have provided me extensive experience in:
    1. Many Computer Vision tasks (e.g. object detection, action recognition, semantic segmentation).
    2. The processing of different data modalities (e.g. RGB, text, point clouds, event and hyperspectral data).
    3. Neural Network design and implementation.
    4. Modern learning strategies (e.g. self-supervised, contrastive, weakly-supervised, multi-modal learning).
    5. Counts with publications in top-rated conferences and journals, as well as industry experience
  • Training in risk management and other applicable: Not required.
  • Valuation: QUALIFIED
  • Date: 09/02/2023
  • Approved by: Alfonso Medela (JD-005 Technical Manager & PRRC)

María Belén Hirigoity​

  • Job position:
    • Dermatologist at Dermatological Institute Dr. Alonso
    • Medical Advisor at Legit.Health
    • Assistant University Professor at the School of Medicine UBA
  • Education: Medical doctor degree from Universidad del Salvador, Buenos Aires, Argentina, with specializations in Dermatology from Bernardino Rivadavia Acute Care General Hospital. Master's degree in Advanced aesthetic and Laser techniques from the University of Buenos Aires (UBA) and Universidad Cardenal Herrera (CEU UCH), Valencia, Spain.
  • Experience with the product/process/technology: With more than 8 years on the Dermatology field, she also has been given access to the device to test it by herself.
  • Training in risk management and other applicable: Not required
  • Valuation: QUALIFIED
  • Date: 02/07/2023
  • Approved by: Alfonso Medela (JD-005 Technical Manager & PRRC)

Constanza Balboni​

  • Job position:
    • Specialist in Dermatology, Medical Aesthetics, Phlebology & Lymphology.
    • Medical Advisor at Legit.Health
    • Assistant University Professor on Dermatology at the University of Medicine UBA
  • Education: Medical doctor degree at the Austral University Hospital. Specialization in Dermatology from Bernardino Rivadavia Hospital. Several specialization and degree in clinical dermatology, trichology, cosmetic dermatology and phlebology. Master's degree in Medical Aesthetics.
  • Experience with the product/process/technology: With more than 6 years on the Dermatology field, she also has been given access to the device to test it by herself.
  • Training in risk management and other applicable: Not required
  • Valuation: QUALIFIED
  • Date: 02/07/2023
  • Approved by: Alfonso Medela (JD-005 Technical Manager & PRRC)

The clinical team CVs are documented as independent files:

  • The internal employees CVs are archived within our Human Resources tool. The CVs are updated and revalidated yearly and archived as: YYYY_MM_Name_Surname_CV
  • The external evaluators CVs are archived and saved in the corresponding evidences folder for the CER placed at GoogleDrive Supporting documentation for the QMS folder.

Additionally, the T-015-007 Declaration of interest Clinical evaluation team of each evaluator is signed and placed at the TF Clinical evaluation folder.

Clinical evaluation team justification​

Selecting the right team for a Clinical Evaluation is a critical decision as the team's composition can greatly influence the quality and impartiality of the evaluation. The team's composition was carefully considered and selected to ensure a thorough and unbiased evaluation of the medical device.

JD-003 and JD-005:​

  • Technical Expertise: The Technical Manager and Design and Development Manager, as founders of the company, have in-depth knowledge of the device's technical aspects. Their involvement ensures that the evaluation benefits from their comprehensive understanding of the device's design, development, and intended use. They can provide valuable insights into the device's technical performance and potential improvements.
  • Ownership and Accountability: As founders, they have a vested interest in the device's success and safety. Their involvement underscores their commitment to the quality and safety of the product. Their accountability ensures that the evaluation process is conducted rigorously.

JD-004:​

  • Regulatory Compliance and Quality Assurance: The Quality Manager and PRRC bring regulatory expertise to the evaluation process. Their presence is crucial for ensuring that the evaluation aligns with regulatory requirements and quality standards. They can verify that the device complies with essential regulatory and quality criteria.
  • Risk Assessment and Mitigation: Their understanding of quality and regulatory compliance can help identify and address potential risks, contributing to the safety and performance of the device.

JD-009:​

  • Data Analysis and Interpretation: The Medical Data Science Professional plays a key role in analyzing clinical data and drawing meaningful insights from it. Their expertise ensures that the data is evaluated objectively and rigorously, contributing to the credibility of the report.
  • Evidence-Based Conclusions: Their involvement adds a layer of scientific rigor to the evaluation process, making sure that conclusions are evidence-based and supported by sound data analysis.

External Healthcare Professionals:​

  • Independence and Objectivity: Including external healthcare professionals in the evaluation team enhances the objectivity and independence of the assessment. They provide an external perspective and reduce the potential for bias or conflicts of interest.
  • Clinical Relevance: Healthcare professionals bring clinical expertise to the table, ensuring that the clinical benefits and safety of the device are assessed from a medical perspective.

Dates and signatures​

Approval an acceptance of the present report by representing the manufacturer of the medical device/s covered by it:

NamePositionSignature
Alfonso MedelaTechnical Responsible

Signature meaning

The signatures for the approval process of this document can be found in the verified commits at the repository for the QMS. As a reference, the team members who are expected to participate in this document and their roles in the approval process, as defined in Annex I Responsibility Matrix of the GP-001, are:

  • Author: Team members involved
  • Reviewer: JD-003, JD-004
  • Approver: JD-001
María Belén HirigoityDermatologist. Medical Advisor at Legit.Health

Signature meaning

The signatures for the approval process of this document can be found in the verified commits at the repository for the QMS. As a reference, the team members who are expected to participate in this document and their roles in the approval process, as defined in Annex I Responsibility Matrix of the GP-001, are:

  • Author: Team members involved
  • Reviewer: JD-003, JD-004
  • Approver: JD-001
Constanza BalboniDermatologist. Medical Advisor at Legit.Health

Signature meaning

The signatures for the approval process of this document can be found in the verified commits at the repository for the QMS. As a reference, the team members who are expected to participate in this document and their roles in the approval process, as defined in Annex I Responsibility Matrix of the GP-001, are:

  • Author: Team members involved
  • Reviewer: JD-003, JD-004
  • Approver: JD-001

Record signature meaning​

  • Author: JD-004 María Diez
  • Review: JD-003 Taig Mac Carthy
  • Approve: JD-005 Alfonso Medela
Previous
R-TF-015-002 Preclinical and clinical evaluation record_2023_001
Next
R-TF-015-007 Delaration of interest Clinical evaluation team_2023_001
  • Scope
  • Summary
  • Scope of the Clinical Evaluation
    • Manufacturer contact details
  • Product characterization
  • Intended use or purpose
  • Variants and models
  • Expected lifetime
  • Clinical Benefits, Outcome Parameters
    • Comprehensive Dermatological Data for Informed Clinical Decisions. Comprehensive Analysis for Informed Decision Making. Improvement in Dermatological Assessment Accuracy. Precise Detection of Dermatological Features. Empowerment of Health Care Practitioners.
    • Faster Measurement of Clinical Signs. Accurate and Objective Measurement of Clinical Signs. Precise Quantification, Count and Extent measure of Skin Issues. Facilitation of Longitudinal Skin Condition Monitoring. Consistent Tracking of Patient Condition Over Time. More agile follow-up consultations.
    • Targeted Analysis for Facial Palsy Evaluation. Support in Assessing Facial Palsy. Accurate Assessment for Facial Palsy.
    • Improved Operational Efficiency for Healthcare Organizations. Streamlining Healthcare Operations. Data-Driven Insights for Workflow Optimization.
    • Support in Preliminary ICD Classification through Image Analysis. Aiding in ICD Classification. Diagnostic Support.
  • Claims foreseen by the manufacturer
    • Clinical performance claims
    • Clinical safety claims
  • Physical and chemical description
  • Incorporation of medicinal substances, tissues or blood products
  • Mechanical and physicochemical characteristics
  • Other characteristics
  • Device pictures, drawings and/or schemes
  • Technologies used
    • Quantification of intensity, count and extent of visible clinical signs
    • Image-based recognition of visible ICD classes
      • Data collection, model training and validation
      • Model calibration
      • Test-time augmentation (TTA)
    • Image quality assessment
    • Image domain check
  • Intended application of device
  • Applicable standards and regulations
    • Regulations
    • Common specifications
    • Harmonized standards
  • Available options
    • Dermatological assessment
      • Measuring the severity
        • Clinical scales (e.g., PASI, SCORAD...)
      • Diagnosing
        • Dermoscopy
        • Skin Biopsy
        • Other imaging techniques (e.g., MRI, CT scans)
      • Therapeutic options
        • Topical Treatments
        • Systemic Medications
        • Phototherapy
    • Facial Palsy Assessment
      • Clinical Scales (e.g., House-Brackmann scale)
      • Diagnosing
        • Electromyography (EMG)
        • Imaging techniques (e.g., MRI, CT scans)
    • Conclusion
  • Clinical Safety, Methods for Analysis
    • Safety-related product claims
  • Benefits and risks. Acceptability of Benefit-Risk-Ratio and undesirable side-effects
    • Benefits
      • Clinical advantages
      • Efficiency and workflow improvement
      • Support for longitudinal monitoring
      • Preliminary diagnostic assistance
    • Risks and undesirable side-effects
      • Data privacy and security
      • Over-reliance on the tool
      • Misinterpretation of data
    • Acceptability of Benefit-Risk ratio
      • Criteria for acceptance
      • Target populations and clinical contexts
      • Professional opinions and unmet medical needs
    • Conclusion
  • Risk Analysis and minimization and management of side effects and other risks
  • Type of evaluation of the device
  • Clinical Background, Current Knowledge, State of the Art
    • Clinical Background & Current Knowledge
      • Overview of Dermatological Conditions and Facial Palsy
        • Dermatoses
          • Epidemiology
          • Aetiology
          • Pathophysiology
          • Investigation and diagnosis
        • Facial palsy
          • Epidemiology
          • Aetiology
          • Pathophysiology
          • Investigation and diagnosis
      • Current Treatment Options
        • Dermatoses
        • Facial palsy
      • Similar devices in the market
    • Literature Search strategy
      • Data sources
        • Clinical data sources
      • Search filters
      • Research questions and keywords
      • Database search steps
        • Step 1: Use PICO to formulate the search strategy.
          • PICO search terms for skin structure-related articles:
          • PICO search terms for facial palsy-related articles:
          • PICO search strategy
        • Step 2: Search parameters
      • Step 3: Data screening
  • Device equivalence
    • 1. Technical characteristics
    • 2. Biological characteristics
    • 3. Clinical Characteristics
    • Summary
  • Data generated and held by the manufacturer
    • Technical and pre-clinical data generated and held by us
    • Clinical data generated and held
  • Clinical Data
    • Clinical Data From Literature
    • Appraisal of clinical data
      • Skin structures analysis
      • Facial palsy analysis
    • Clinical Data from Clinical Study Databases
    • Clinical Data From Adverse Event Databases
    • Analysis of the Clinical Data
      • Requirement on safety
        • Special design features and safety concerns
        • Addressing risks from risk management documentation and literature
        • Training and user precautions
        • Consistency with current knowledge and state of the art
        • Cybersecurity
      • Requirement on acceptable Benefit/Risk
        • Total experience with the device
          • Clinical investigations and PMCF
          • User experience and market exposure
        • Nature and extent of benefits and risks
          • Benefits to the patients
          • Undesirable side-effects and other Rrisks
        • Evaluation of the benefit/risk profile
        • Acceptability of benefits and risks across medical conditions and target populations
      • Requirement on performance
        • Achieving Intended Performance during Normal Conditions of Use
        • Support by Sufficient Clinical Evidence
          • Valid Clinical Association
          • Technical Performance
          • Clinical Performance
        • Conclusion
      • Requirement on acceptability of undesirable side-effects
        • Evaluation of available data
        • Acceptability of undesirable side-effects
        • Conclusion
  • Post-Market Activities
    • Reference to Key Documents and Records
      • GP-007 Post-market Surveillance
      • T-007-001 Post-market Surveillance (PMS) Plan
      • T-007-002 Post-market Clinical Follow-up (PMCF) Plan
      • T-007-003 Periodic Safety Update Report (PSUR)
      • T-007-004 PMS Evaluation Report
      • T-007-005 PMCF Evaluation Report
    • Data Tracking for Clinical Benefits and Safety Verification
  • GAP analysis
    • Validation and Evidence of Clinical Safety and Performance
      • Clinical Safety
      • Clinical Performance
      • Benefit/Risk Ratio
    • Relation with Market Experience and Literature
      • Market Experience
      • Literature and State-of-the-Art Comparison
    • PMCF Activities
  • Conclusions
    • Validation of Safety and Performance
    • Validation of User Information
    • Conclusion Table for GSPR
  • Date of the Next Clinical Evaluation
  • Qualification of the Responsible Evaluators
    • Taig Mac Carthy
    • Alfonso Medela
    • María Diez
    • Alberto Sabater
    • María Belén Hirigoity
    • Constanza Balboni
    • Clinical evaluation team justification
      • JD-003 and JD-005:
      • JD-004:
      • JD-009:
      • External Healthcare Professionals:
  • Dates and signatures
  • Record signature meaning
All the information contained in this QMS is confidential. The recipient agrees not to transmit or reproduce the information, neither by himself nor by third parties, through whichever means, without obtaining the prior written permission of Legit.Health (AI LABS GROUP S.L.)