R-TF-015-006 Clinical investigation report
Research Title
Non-invasive prospective Pilot in a Live Environment for the improvement of the diagnosis of skin pathologies in primary care.
Product Identification
| Information | |
|---|---|
| Device name | Legit.Health Plus (hereinafter, the device) |
| Model and type | NA |
| Version | 1.1.0.0 |
| Basic UDI-DI | 8437025550LegitCADx6X |
| Certificate number (if available) | MDR 792790 |
| EMDN code(s) | Z12040192 (General medicine diagnosis and monitoring instruments - Medical device software) |
| GMDN code | 65975 |
| Class | Class IIb |
| Classification rule | Rule 11 |
| Novel product (True/False) | FALSE |
| Novel related clinical procedure (True/False) | FALSE |
| SRN | ES-MF-000025345 |
Promoter Identification and Contact
| Manufacturer data | |
|---|---|
| Legal manufacturer name | AI Labs Group S.L. |
| Address | Street Gran Vía 1, BAT Tower, 48001, Bilbao, Bizkaia (Spain) |
| SRN | ES-MF-000025345 |
| Person responsible for regulatory compliance | Alfonso Medela, María Diez, Giulia Foglia |
| office@legit.health | |
| Phone | +34 638127476 |
| Trademark | Legit.Health |
Identification of sponsors
Instituto de Investigación Sanitaria Puerta de Hierro
Fundación de Investigación Biomédica del Hospital Universitario Puerta del Hierro de Majadahonda. C/ Joaquín Rodrigo, 2 - 28222 Majadahonda - Madrid, Spain.
Clinical Investigation Plan (CIP) Identification
- Title: Non-Invasive Prospective Pilot in a Live Environment for the improvement of the diagnosis of skin pathologies in primary care.
- Protocol code: LEGIT.HEALTH_PH_2024.
- Study design: Prospective observational analytical and cross-sectional study.
- Product under investigation: Legit.Health Plus.
- Version and date: Version 1.0, 2024-07-04.
Public Access Database
Please note that the database used in this study is not publicly accessible due to privacy and confidentiality considerations.
Research Team
Principal investigator
- Dr Gastón Roustan Gullón
Collaborators
- Medical staff
- Dr Adriana Vasconcelos
- Dr María Porriño
- Dr Gustavo
- Dr Josefina Sanz
- Dr Andrés
- Dr Gerald Selda
- Dr Helena Bahachille
- Dr Mitchell Ignacio Leal Betancourt
- Dr Marianela del Castillo
- Dr María Pilar Martínez Marta
- Dr Nadia Hayajneh Carrillo
- Dr Carmen Arsuaga
- Dr Elena Sánchez Largo
- Dr María Gómez
- Dr Pedro Ortega Lozano
- AI Labs Group S.L.
- Mr Alfonso Medela
- Mr Taig Mac Carthy
- Mrs Alba Rodríguez
Center
- This study was conducted remotely by sending the images to the participating dermatologists.. Although all the primary care physicians were affiliated to Las Rozas Health care centre.
Compliance Statement
The clinical investigation was perforfed according to the Clinical Investigation Plan (CIP) and other applicable guidances and regulations. This includes compliance with:
- Harmonized standard
UNE-EN ISO 14155:2021 Regulation (EU) 2017/745 on medical devices (MDR)- Harmonized standard
UNE-EN ISO 13485:2016s Regulation (EU) 2016/679(GDPR).- Spanish
Organic Law 3/2018on the Protection of Personal Data and guarantee of digital rights`.
All data processing within the device is carried out in accordance with the highest standards of data protection and privacy. Patient information is managed in an encrypted manner to ensure confidentiality and security.
The research team assumes the role of Data Controller, responsible for the collection and management of study data. Legit.Health acts as the Data Processor and is not involved in the processing of patient data.
The storage and transfer of data comply with European data protection regulations. At the conclusion of the study, all information stored in the device will be permanently and securely deleted.
The device employs robust technical and organizational security measures to safeguard personal data against unauthorized access, alteration, loss, or processing.
Report Date
October 13th, 2024
Report author(s)
The full name, the ID and the signature for the authorship, as well as the approval process of this document, can be found in the verified commits at the repository. This information is saved alongside the digital signature, to ensure the integrity of the document.
Table of contents
Table of contents
- Research Title
- Product Identification
- Promoter Identification and Contact
- Identification of sponsors
- Clinical Investigation Plan (CIP) Identification
- Public Access Database
- Research Team
- Compliance Statement
- Report Date
- Report author(s)
- Table of contents
- Abbreviations and Definitions
- Summary
- Introduction
- Material and methods
- Results
- Discussion and Overall Conclusions
- References
- Investigators and Administrative Structure of Clinical Research
- Report Annexes
Abbreviations and Definitions
- AE: Adverse Event
- AEMPS: Spanish Agency of Medicines and Medical Devices
- AEP: Adverse Reaction to Product
- AUC: Area Under the ROC Curve
- CAD: Computer-Aided Diagnosis
- CMD: Data Monitoring Committee
- CIP: Clinical Investigation Plan
- CUS: Clinical Utility Questionnaire
- DLQI: Dermatology Quality of Life Index
- GCP: Standards of Good Clinical Practice
- ICH: International Conference of Harmonization
- IFU: Instructions For Use
- IRB: Institutional Review Board
- N/A: Not Applicable
- NCA: National Competent Authority
- PI: Principal Investigator
- SAE: Serious Adverse Events
- SAEP: Serious Adverse Event to Product
- SUAEP: Serious and Unexpected Adverse Event to the Product
- SUS: System Usability Scale
Summary
Title
Non-invasive prospective Pilot in a Live Environment for the improvement of the diagnosis of skin pathologies in primary care
Introduction
Dermatological conditions represent a significant portion of primary care consultations, constituting approximately 5% of all visits. However, discrepancies between diagnoses made by primary care physicians and dermatologists remain substantial, with concordance rates between 57% and 65.52%. These discrepancies can lead to misdiagnoses or incorrect referrals, which affect the quality of life of patients with skin pathologies. This investigation pretends to assess whether the use of the Legit.Health medical device can improve the accuracy of the diagnosis of different skin conditions in primary care. 9 primary care physicians were recruited and each one of them will be presented with 30 images to review. This research follows rigorous ethical standards and adherence to regulatory guidelines, this research holds significant potential in revolutionizing dermatological diagnostics.
Objectives
Primary objective
- To validate that the information provided by the device increases the true accuracy of primary care physicians in the diagnosis of multiple dermatological conditions.
Secondary objectives
- To validate what percentage of cases should be referred according to the HCP with the information provided by the device.
- To validate what percentage of cases could be handled remotely with the information provided by the device.
Population
In this study, the population will consist of primary care physicians. A minimum of 9 physicians will be selected to participate in this study and review the images.
Sample size
Sample size
The objective of this study is to evaluate whether the use of Legit.Health improves diagnostic accuracy by at least 10% among both dermatologists and primary care physicians, providing evidence to support its integration into clinical practice. To achieve this, a sample of 9 physicians will each review 30 dermatological images, resulting in a total of 270 evaluations. This sample size was determined to balance practical constraints with the need for sufficient statistical power to detect clinically meaningful improvements.
The selection of 9 physicians aims to minimize the impact of inter-observer variability while maintaining a manageable cohort for analysis. By focusing on a smaller group, the study reduces the potential for inconsistencies associated with larger, more heterogeneous physician samples. Each physician will evaluate a substantial number of images, ensuring adequate exposure to diverse clinical scenarios and enabling a comprehensive assessment of the device's utility.
The 30 images per physician were chosen to provide a representative range of dermatological conditions, ensuring that the device's performance is evaluated across varied patient profiles. Images will be selected to reflect real-world clinical diversity, encompassing cases of differing severity and complexity. This design ensures that physicians' evaluations are both practical and relevant to real-world applications, enhancing the reliability of the findings.
By concentrating on a focused, experienced cohort of physicians and a robust volume of images per participant, the study design enhances its ability to detect statistically significant improvements in diagnostic accuracy. The chosen configuration also ensures high-quality data collection, supporting meaningful conclusions about the potential benefits of integrating Legit.Health into clinical workflows.
Design and Methods
Design
This investigation proceeds as follows:
Healthcare practitioners' recruitment and image presentation
We developed a website for conducting the experiment. Primary care physicians were required to log in to the website initially. Subsequently, they were presented with a series of questions structured as follows:
- Based on the provided image, what diagnosis do you consider most appropriate? This question was accompanied by anamnesis inquiries regarding allergies, ongoing treatments, and other relevant medical history.
- Considering both the image and the analysis provided by the AI, what diagnosis do you deem most appropriate? In this instance, the same information from question 1 was supplemented with the top 5 diagnoses and their respective confidence levels, calculated by Legit.Health's diagnosis support algorithm based on the image.
- Based on information provided by the AI, does this patient require a dermatology referral? Additional information such as the malignancy index and a referral recommendation by the tool were provided in this step.
- According to the information provided by the AI, can remote diagnosis and treatment be confirmed? No additional information was provided at this stage.
Each participant was presented with 30 cases or images to review. These images had been previously confirmed by dermatologists and by anatomical pathology for cases of skin cancer. The conditions were distributed as follows:
| Condition | ICD-11 code | Number of images |
|---|---|---|
| Atypical melanocytic nevus | 2F20.1 | 2 |
| Melanocytic nevus | 2F20 | 3 |
| Melanoma | 2C30 | 5 |
| Basal cell carcinoma | 2C32 | 3 |
| Urticaria | EB05 | 5 |
| Pustular psoriasis | EA90.4 | 2 |
| Actinic keratosis | EK90.0 | 2 |
| Plaque psoriasis | EA90.0 | 3 |
| Hidradenitis suppurativa | ED92.0 | 5 |
All this information was recorded in a database and exported to a CSV file, which was subsequently used for further analysis. The analysis was conducted using the Python programming language and statistical libraries such as numpy and pandas. Statistical measures, including the P-Value, were calculated to either accept or reject the hypothesis. Additionally, metrics were calculated based on pathology and medical specialization. Atypical melanocytic nevus and melanocytic nevus were evaluated together under a single category: XH4L78 Pigmented Nevus referred to in the following sections simply as "nevus".
Number of subjects
A total of 9 HCPs of primary care were recruited in this study.
Initiation Date
June 04rd, 2024
Completion Date
September 13rd, 2024
Duration
The study spanned 3 months, encompassing the time needed for tasks such as the creation of the website, database closure and editing, data analysis, and the preparation of the final study report after the recruitment of the last subject.
Methods
The study employed a prospective observational analytical and cross-sectional design to evaluate whether the use of Legit.Health improved the accuracy of the diagnosis of different skin pathologies by HCPs. This investigation encompassed a diverse cohort of 9 primary care physicians. Data collection included the accuracy of the diagnosis of different pathologies with and without the use of Legit.Health: if Legit.Health reinforces, improves, worsens or has no impact on the diagnosis made by the physicians. The study adhered to strict ethical guidelines, ensuring patient confidentiality and compliance with international standards.
Results
For this study, nine primary care doctors reviewed all the images, which were 30.
When HCPs did not use Legit.Health they showed an accuracy of 72.96% on their diagnosis. On the other hand, when they integrated Legit.Health on their diagnosis, the accuracy increased to 82.22%. This increase was observed for all targeted conditions, excluding basal cell carcinoma.
In relation to the referrals, 48.89% of cases did not needed a referral. Finally, regarding remote consultations 60.74% of cases can be handled remotely.
Conclusions
Legit.Health has proven to be an effective tool for enhancing diagnostic accuracy among primary care physicians, boosting their accuracy rate from 72.96% to 82.22%. Although improvements varied by skin condition, notable gains were observed in cases such as hidradenitis suppurativa, urticaria, and actinic keratosis. However, statistical significance was not achieved across all pathologies due to limited sample sizes.
The tool also helped reduce unnecessary referrals, with 49% of cases managed effectively without specialist consultation, contrasting with previous studies showing higher referral rates to dermatology. Furthermore, Legit.Health facilitated remote management in 60.74% of cases, suggesting that diagnostic support tools like this one can play a critical role in promoting efficient remote consultations and streamlining triage processes in primary care settings.
Introduction
Dermatological conditions represent a significant portion of primary care consultations, constituting approximately 5% of all visits. However, discrepancies between diagnoses made by primary care physicians and dermatologists remain substantial, with concordance rates between 57% and 65.52%. This gap in expertise often leads to misdiagnoses, incorrect referrals, and delays in appropriate treatment, particularly in rare and severe conditions. The limited availability of dermatologists, especially in rural areas, further complicates patient care, underscoring the need for innovative solutions to optimize resource allocation and improve diagnostic accuracy.
Teledermatology has shown promise in reducing the pressure on in-person consultations by enabling remote assessments. However, the use of artificial intelligence (AI) presents a transformative opportunity to enhance the diagnostic capabilities of primary care physicians. Legit.Health, an AI-based medical device, has already been validated in the diagnosis of skin conditions and offers advanced tools, such as the automatic scoring of diverse pathologies. This pilot study aims to evaluate whether the use of the Legit.Health medical device can increase the true accuracy of healthcare professionals (HCPs), especially in primary care, in the diagnosis of multiple dermatological conditions.
Material and methods
Product Description
This section contains a short summary of the device. A complete description of the intended purpose, including device description, can be found in the record Legit.Health Plus description and specifications.
Product description
The device is a 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 purpose
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) categories.
Intended previous uses
No specific intended use was designated in prior stages of development.
Product changes during clinical research
The device maintained a consistent performance and features throughout the entire clinical research process. No alterations or modifications were made during this period.
Clinical Investigation Plan
Objectives
To validate that the information provided by the device increases the true accuracy of primary care physicians in the diagnosis of multiple dermatological conditions.
Design (type of research, assessment criteria, methods, active group, and control group)
This is a prospective observational and cross-sectional study. The study does not involve an active or control group, as the physicians will be their control group, firstly without using Legit.Health and after making their diagnosis, using Legit.Health to assess if they want to change their diagnosis or keep it. The assessment criteria include the assessment of different images with pathologies such as GPP or HS and their diagnosis. The study employs a variety of methods, including data collection through websites or photograph analysis.
Ethical considerations
This study adhered to international Good Clinical Practice (GCP) guidelines, the Declaration of Helsinki in its latest amendment, and applicable international and national regulations. As applicable, approval from the relevant Ethics Committee was obtained prior to the initiation of the study. When applicable, modifications to the protocol were reviewed and approved by the Principal Investigator (PI) and subsequently evaluated by the Ethics Committee before subjects were enrolled under a modified protocol.
This study was conducted in compliance with European Regulation 2016/679, of 27 April, concerning the protection of natural persons with regard to the processing of personal data and the free movement of such data (General Data Protection Regulation, GDPR), and Organic Law 3/2018, of 5 December, on the Protection of Personal Data and the guarantee of digital rights. In accordance with these regulations, no data enabling the personal identification of participants was collected, and all information was managed securely in an encrypted format.
Participants were informed both orally and in writing about all relevant aspects of the study, with the information being tailored to their level of understanding. They were provided with a copy of the informed consent form and the accompanying patient information sheet. Adequate time was given to patients to ask questions and fully comprehend the details of the study before providing their consent.
The PI was responsible for the preparation of the informed consent form, ensuring it included all elements required by the International Conference on Harmonisation (ICH), adhered to current regulatory guidelines, and complied with the ethical principles of GCP and the Declaration of Helsinki.
The original signed informed consent forms were securely stored in a restricted access area under the custody of the PI. These documents remained at the research site at all times. Participants were provided with a copy of their signed consent form for their records.
Data Quality Assurance
The Principal Investigator is responsible for reviewing and approving the protocol, signing the Principal Investigator commitment, guaranteeing that the persons involved in the centre will respect the confidentiality of patient information and protect personal data, and reviewing and approving the final study report together with the sponsor. All the clinical members of the research team assess the eligibility of the patients in the study, inform and request written informed consent, collect the source data of the study in the clinical record and transfer them to the Data Collection Notebook (DCN) or Data Collection Forms (CRF).
Subject Population
This study enrolled primary care physicians to review the images and make a diagnosis. At the end of the study, 9 HCPs were enrolled. These doctors should evaluate 30 images of different skin pathologies and diagnose them.
Inclusion Criteria
- Board-certified primary care physicians and dermatologists, regardless of their professional experience.
- High-quality images of patients with different skin conditions.
Exclusion Criteria
- Low-quality images of patients which can not be properly analyzed.
Statistical Analysis
To estimate the correlation between diagnoses with and without using Legit.Health, we analyzed the concordance between diagnoses for both primary care physicians and dermatologists. We analyzed if Legit.Health reinforced the diagnosis of the physicians (after observing the results of Legit.Health, the doctor maintains the diagnosis when his answer matches that of the solution) if Legit.Health improves the practitioner's diagnosis (the doctor changes the diagnosis when his answer does not match that of the solution), if it has no impact (The doctor does not change the diagnosis even though his answer does not match that of the solution) and if it has a negative impact (The doctor changes the diagnosis for an answer that does not match that of the solution). We also analyzed if physicians considered if this case should be referred to the specialist or it could be handled remotely.
Results
Initiation and Completion Date
June 04rd, 2024 September 13rd, 2024
Subject and Investigational Product Management
A total of 9 primary care physicians were involved in this study. Each physician was presented with 30 images in order to review them and make a diagnosis of the pathology. Furthermore, they should indicate if these patients should be referred to dermatology or handled in primary care, or also if it can be handled remotely. This meticulous selection process ensures the integrity and validity of the study's findings. The investigational products were stored and handled following strict protocols. This included proper storage conditions, handling procedures, and documentation of product usage. The accountability and traceability of investigational products were rigorously maintained throughout the study.
Subject Demographics
The study did not place specific emphasis on gender, age, or nationality as primary factors of investigation. Instead, it encompassed a diverse physician cohort with different specialities.
Clinical Investigation Plan (CIP) Compliance
The study adhered to all aspects outlined in the CIP. This ensured that the research was conducted in accordance with established protocols, procedures, and ethical standards. Any deviations from the CIP were duly documented and appropriately addressed. Compliance with the CIP was rigorously monitored throughout the study to uphold the integrity and validity of the research findings.
Analysis
Primary Analyses
Diagnosis
In this study, nine primary care doctors reviewed all the images.
We conducted a McNemar test in order to analyze if the information provided by Legit.Health impacts on the healthcare professionals' diagnostics'. Overall, primary care doctors demonstrated an accuracy of 72.96%, which notably increased to 82.22% with the integration of Legit.Health. Our analysis, supported by a very low p-value (0.0001), revealed the following key findings:
- Legit.Health reinforces practitioners' diagnostics in 69.26% of the cases
- Legit.Health improves practitioners' diagnostics in 12.96% of the cases
- Legit.Health does not have any impact on practitioners' diagnostics in 14.07% of the cases
- Legit.Health has a negative impact on practitioners' diagnostics in 3.70% of the cases
An analysis by pathology identified significant impacts for certain conditions, as detailed in the table below:
| Condition | Accuracy (%) | Accuracy with Legit.Health (%) | Relative difference (%) | p-value |
|---|---|---|---|---|
| Actinic keratosis | 55.56 | 83.33 | 49.98 | 0.125 |
| Pustular psoriasis | 5.56 | 22.22 | 299.64 | 0.25 |
| Plaque psoriasis | 96.30 | 96.30 | 0.00 | 1.00 |
| Nevus | 75.56 | 77.78 | 2.91 | 1.00 |
| Melanoma | 86.67 | 91.11 | 5.12 | 0.69 |
| Urticaria | 73.33 | 91.11 | 24.24 | 0.02 |
| Hidradenitis suppurativa | 64.44 | 80.00 | 24.14 | 0.04 |
| Basal cell carcinoma | 91.67 | 88.89 | -3.00 | 1.00 |
Referral
Assessing the impact of Legit.Health on referrals, our findings revealed that 48.89% of cases did not necessitate a referral.
| Condition | Do not require referral (%) |
|---|---|
| Nevus | 60.00 |
| Melanoma | 2.22 |
| Basal cell carcinoma | 7.41 |
| Urticaria | 88.89 |
| Pustular psoriasis | 11.11 |
| Actinic keratosis | 33.33 |
| Plaque psoriasis | 81.48 |
| Hidradenitis suppurativa | 71.11 |
Remote consultations
Furthermore, we examined the feasibility of handling cases remotely through teledermatology. The results show that 60.74% of the cases can be handled remotely.
Conducting a Pearson's chi-squared test on the necessity for referrals and teleconsultations, we concluded with 95% confidence that a strong association exists between referrals and remote consultations. Specifically:
- 36.67% of the cases do not require a referral and can have follow-up remotely
- 12.22% of the cases do not require a referral but require an in-person appointment
- 24.07% of the cases require a referral and remote consultation
- 27.04% of the cases require a referral in addition to an in-person appointment
| Pathology | Can be handled remotely (%) |
|---|---|
| Nevus | 55.56 |
| Melanoma | 42.22 |
| Basal cell carcinoma | 44.44 |
| Urticaria | 75.56 |
| Pustular psoriasis | 38.89 |
| Actinic keratosis | 61.11 |
| Plaque psoriasis | 81.48 |
| Hidradenitis suppurativa | 75.56 |
Adverse Events and Adverse Reactions to the Product
Throughout the study, no adverse events or adverse reactions related to the investigational product have been observed. Participants have not experienced any negative reactions or side effects associated with the use of the product. This indicates a favourable safety profile of the investigational product in the context of this study.
Product Deficiencies
No deficiencies in the product have been observed during the course of this study. As a result, no corrective actions have been deemed necessary. The product has demonstrated consistent performance in accordance with the study's objectives.
Subgroup Analysis for Special Populations
In the context of the analyzed pathologies, no special population subgroups were identified for this study. The research primarily focused on the specified physician population without subgroup differentiation.
Discussion and Overall Conclusions
Conclusions
Legit.Health significantly enhanced primary care physicians' diagnostic accuracy, increasing it from 72.96% to 82.22%.
The impact of Legit.Health varied across different skin conditions, demonstrating significant improvements in hidradenitis suppurativa, urticaria, and actinic keratosis. However, p-values were not statistically significant for all cases, due to the low number of samples per pathology.
Approximately 49% of cases did not necessitate a referral. Additionally, 60.74% of cases across all specialities could be managed remotely.
Reduction of referral and use of remote consultation
Previous studies reported that 66% of patients visiting primary care HCPs are referred to dermatology, with very low (1%) remote consultation rates (González-López et al., 2019). In terms of urgent referral and triage, some institutions have reported that 76.8% of patients referred from primary HCPs to dermatology result in benign diagnoses (Pagani et al., 2023).
In this experiment, we found that 49% of cases should be referred according to the primary HCP with the information provided by the device, which is 17% lower than the aforementioned referral rates. Additionally, our results improve the remote consultation rates, suggesting that diagnostic support tools can help foster remote consultations.
References
- González-López G, Descalzo-Gallego MÁ, Arias-Santiago S, Molina-Leyva A, Gilaberte Y, Fernández-Crehuet P, Husein-El Ahmed H, Viera-Ramírez A, Fernández-Peñas P, Taberner R, García-Doval I. Derivación de pacientes en consulta de dermatología y de teledermatología en España. Estudio DIADERM. Actas Dermo-Sifiliográficas. 2019 Mar 1;110(2):146-52.
- Pagani K, Lukac D, Olbricht SM, Aronson MD, Benneyan JC, Fernandez L, Salant T, Schiff GD, Shafiq U, Sternberg SB, McGee JS. Urgent referrals from primary care to dermatology for lesions suspicious for skin cancer: patterns, outcomes, and need for systems improvement. Archives of dermatological research. 2023 Jul;315(5):1397-400.
Implications for Future Research
The positive outcomes of this study pave the way for several avenues of future research. Firstly, helping to improve the diagnosis of different skin pathologies, which can significantly impact the quality of life of patients who suffer from them.
On the other hand, exploring the integration of artificial intelligence and machine learning techniques to refine the tool's diagnostic capabilities warrants attention. This could lead to even more accurate and reliable assessments, potentially revolutionizing the field of dermatology.
Additionally, conducting long-term studies to evaluate the impact of the device on patient outcomes, including treatment adherence and quality of life, would provide a comprehensive understanding of its broader clinical implications.
Limitations of Clinical Research
The main limitations of the pilot include several factors that may influence the perception and effectiveness of the AI-based device. Firstly, the acceptance and trust of healthcare professionals in these emerging technologies can vary significantly. The device's effectiveness may be compromised if users are not fully convinced of its accuracy or usefulness, thereby affecting the overall perception of its performance.
Additionally, image quality is crucial for the device's performance. Issues such as low-quality photographs, errors in cropping lesions, or variations in lighting and focus can deteriorate the quality of the data received by the system, which may negatively influence the evaluation and perception of its effectiveness by the researchers.
Variability in image conditions is also an important aspect to consider. Differences in lighting, colour, shape, size, and focus of the images, along with the number of images available for each patient, can affect the accuracy of the results. High variability in images of the same patient or an insufficient number of representative images can decrease the expected diagnostic accuracy of the device.
Additionally, the consistency of investigators in using Legit.Health is crucial. Variations in how diligently investigators use the device can impact the pilot's findings. If the investigators are not consistent in their use of the device, it can lead to unreliable results and affect the overall assessment of its efficacy.
Another limitation is the Hawthorne effect, where pilot subjects may change their behaviour simply because they know they are being observed. This awareness can influence their decisions and actions within the pilot, potentially skewing the results and not accurately reflecting how the device would be used in a non-study environment.
Ethical Aspects of Clinical Research
The conduct of this study adheres to international Good Clinical Practice standards and is in compliance with the Declaration of Helsinki in its latest active amendment. It also conforms to international and national rules and regulations.
The study did not require approval from an Ethics Committee due to its observational nature and the fact that it does not allow for patient identification.
The study was conducted following European Regulation 2016/679, of 27 April, on the protection of natural persons about the processing of personal data and the free movement of such data. Additionally, it adheres to the Spanish Organic Law 3/2018, of 5 December, on the Protection of Personal Data and guarantee of digital rights concerning data processing. No data that allows the personal identification of subjects has been included, and all information managed was encrypted.
Physicians have received comprehensive oral and written information about the study tailored to their level of understanding. The main investigator ensured that the participants had sufficient time to ask questions and clarify any doubts regarding the study details.
The original signed informed consent form has been securely stored in a restricted access area under the custody of the Principal Investigator. A copy of the original signed consent form has been given to the patient.
The Data Controller for this study is the research team. Legit.Health, the Data Processor, is not responsible for processing the data included in the Software or its users. The storage and handling of data and photographs is aligned with the European Regulation 2016/679 of 27 April on the protection of natural persons about the processing of personal data and the free movement of such data and the Organic Law 3/2018 of 5 December on the Protection of Personal Data and guarantee of digital rights. At the conclusion of the study, all information stored in the device will be completely and permanently deleted.
The device complies with current legislation on the protection and confidentiality of personal data. Appropriate technical and organizational security measures are in place to ensure the security of personal data and prevent its alteration, loss, unauthorized processing or access.
Investigators and Administrative Structure of Clinical Research
Brief Description
This CIP has been conducted between the participating medical staff, the Instituto de Investigación Sanitaria Puerta de Hierro and AI Labs Group S.L. (Legit.Health).
Investigators
Principal investigator
- Dr Gastón Roustán Gullón
Collaborators
- Medical staff
- Dr Adriana Vasconcelos
- Dr María Porriño
- Dr Gustavo
- Dr Josefina Sanz
- Dr Andrés
- Dr Gerald Selda
- Dr Helena Bahachille
- Dr Mitchell Ignacio Leal Betancourt
- Dr Marianela del Castillo
- Dr María Pilar Martínez Marta
- Dr Nadia Hayajneh Carrillo
- Dr Carmen Arsuaga
- Dr Elena Sánchez Largo
- Dr María Gómez
- Dr Pedro Ortega Lozano
- AI Labs Group S.L.
- Mr Alfonso Medela
- Mr Taig Mac Carthy
- Mrs Alba Rodríguez
External Organization
No additional organizations, beyond those previously mentioned, contributed to the clinical research. The study was conducted with the collaboration and resources of the specified entities.
Promoter and Monitor
Instituto de Investigación Sanitaria Puerta de Hierro.
Report Annexes
- Instructions For Use (IFU) can be found in the protocol.
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-005