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  • Quality Manual
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    • GP-001 Control of documents
      • Annex 1 Responsibility matrix
      • Deprecated
      • Templates
        • T-001-001 Control of documents
        • T-001-002 Manufacturer declaration of conformity
        • T-001-005 List of external documents
        • T-001-006 IFU and label validation YYYY_nnn
        • T-001-007 Manufacturer declaration of conformity_MDR
        • T-001-008 _Device name_ label YYYY_nnn
        • T-001-009 Training on procedures of the QMS
      • Specific procedures
    • GP-002 Quality planning
    • GP-003 Audits
    • GP-004 Vigilance system
    • GP-005 Human Resources and Training
    • GP-006 Non-conformity, Corrective and Preventive actions
    • GP-007 Post-market surveillance
    • GP-008 Product requirements
    • GP-009 Sales
    • GP-010 Purchases and suppliers evaluation
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    • GP-013 Risk management
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    • GP-015 Clinical evaluation
    • GP-016 Traceability and identification
    • GP-017 Technical assistance service
    • GP-018 Infrastructure and facilities
    • GP-019 Software validation plan
    • GP-020 QMS Data analysis
    • GP-021 Communications
    • GP-022 Document translation
    • GP-023 Change control management
    • GP-024 Cybersecurity
    • GP-025 Usability and Human Factors Engineering
    • GP-027 Corporate Governance
    • GP-050 Data Protection
    • GP-051 Security violations
    • GP-052 Data Privacy Impact Assessment (DPIA)
    • GP-100 Business Continuity (BCP) and Disaster Recovery plans (DRP)
    • GP-101 Information security
    • GP-200 Remote Data Acquisition in Clinical Investigations
    • GP-026 Market-specific product requirements
  • Records
  • Legit.Health Plus Version 1.1.0.0
  • Legit.Health Plus Version 1.1.0.1
  • Licenses and accreditations
  • External documentation
  • Procedures
  • GP-001 Control of documents
  • Templates
  • T-001-007 Manufacturer declaration of conformity_MDR

T-001-007 Manufacturer declaration of conformity_MDR

AI Labs Group SL, located at the Street Gran Vía 1, BAT Tower, 48001, Bilbao, Bizkaia (Spain), with SRN ES-MF-000025345, certifies under its responsibility that the manufactured product:

Information
Device nameLegit.Health Plus (hereinafter, the device)
Version1.0
Basic UDI-DI8437025550LegitCADx6X
Intended purposeThe 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. The device is designed to be used when a healthcare practitioner chooses to obtain additional information to consider a decision.
Intended purpose

The complete intended purpose can be consulted below, in the section Intended use or purpose.

Complies with the applicable standards:

  • UNE-EN ISO 13485:2016 Quality management systems. Requirements for regulatory purposes.
  • UNE-EN 62304:2007/CORR:2009/A1:2016 Medical device software. Software life cycle processes.
  • UNE-EN ISO 14971:2019 Medical devices/health products. Application of risk management to MD.
  • UNE-EN ISO 15223-1: 2017 Health products. Symbols to be used on labels, labelling and information to be supplied.
  • UNE-EN 1041: 2009/A1:2014 Information provided by the manufacturer of medical devices.
  • UNE-EN 62366:2009/A1:2015 Application of usability engineering to medical devices.

Complies with the following common specification:

  • 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.

And complies with the provisions of the Regulation (EU) 2017/745 of the European Parliament and of the Council on Medical Devices and issued under the exclusive responsibility of AI Labs Group SL.

Classification: Class IIa (Rule 11)

The conformity assessment route is based on a quality management system and on assessment of technical documentation according to the Annex IX or the above mentioned regulation.

Certificate ID: To include

Notified body: BSI (British Standards Institution) number 2797.

All documentation supporting this CE Declaration of Conformity is preserved in the document management system of the manufacturer, supported by the Quality System approval to ISO 13485 by BSI.

This document was issued in Bilbao, on the following date:

Signature and date
General Manager full name

Intended use or purpose​

Intended use

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, enhancing efficiency and accuracy of care delivery, by providing:

  • an interpretative distribution representation of possible International Classification of Diseases (ICD) categories that might be represented in the pixels content of the image
  • quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others

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,
  • xerosis (dryness),
  • swelling (oedema),
  • oozing,
  • excoriation,
  • lichenification,
  • exudation,
  • wound depth,
  • wound border,
  • undermining,
  • hair loss,
  • necrotic tissue,
  • granulation tissue,
  • epithelialization,
  • nodule,
  • papule
  • pustule,
  • cyst,
  • comedone,
  • abscess,
  • draining tunnel,
  • inflammatory lesion,
  • exposed wound, bone and/or adjacent tissues,
  • slough or biofilm,
  • maceration,
  • external material over the lesion,
  • hypopigmentation or depigmentation,
  • hyperpigmentation,
  • scar,
  • ictericia

Image-based recognition of visible ICD categories

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

Device 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 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 qualifications and competencies

This section outlines the qualifications and competencies required for users of the device to ensure its safe and effective use. It is assumed that all users already possess the baseline qualifications and competencies associated with their respective professional roles.

Healthcare professionals

No additional official qualifications are required for healthcare professionals (HCPs) to use the device. However, it is recommended that HCPs possess the following competencies to optimize device utilization:

  • Proficiency in capturing high-quality clinical images using smartphones or equivalent digital devices.
  • Basic understanding of the clinical context in which the device is applied.
  • Familiarity with interpreting digital health data as part of the clinical decision-making process.

The device may be used by any healthcare professional who, by virtue of their academic degree, professional license, or recognized qualification, is authorized to provide healthcare services. This includes, but is not limited to:

  • Medical Doctors (MD, MBBS, DO, Dr. med., or equivalent)
  • Registered Nurses (RN, BScN, MScN, Dipl. Pflegefachfrau/-mann, or equivalent)
  • Nurse Practitioners (NP, Advanced Nurse Practitioner, or equivalent)
  • Physician Assistants (PA, or equivalent roles such as Physician Associate in the UK/EU)
  • Dermatologists (board-certified, Facharzt für Dermatologie, or equivalent)
  • Other licensed or registered healthcare professionals as recognized by local, national, or European regulatory authorities

Each HCP must hold the academic title, degree, or professional registration that confers their status as a healthcare professional in their jurisdiction, whether in the United States, Europe, or other regions where the device is provided.

IT professionals

IT professionals are responsible for the technical integration, configuration, and maintenance of the medical device within the healthcare organization's information systems.

No specific official qualifications are mandated. Nevertheless, it is advisable that IT professionals involved in the deployment and support of the device have the following competencies:

  • Foundational knowledge of the HL7 FHIR (Fast Healthcare Interoperability Resources) standard and its application in healthcare data exchange.
  • Ability to interpret and manage the device's data outputs, including integration with electronic health record (EHR) systems.
  • Understanding of healthcare data privacy and security requirements relevant to medical device integration, including GDPR (Europe), HIPAA (US), and other applicable local regulations.
  • Experience with troubleshooting and supporting clinical software in a healthcare environment.
  • Familiarity with IT standards and best practices for healthcare, such as ISO/IEC 27001 (Information Security Management) and ISO 27799 (Health Informatics—Information Security Management in Health).

IT professionals may include, but are not limited to:

  • Health Informatics Specialists (MSc Health Informatics, or equivalent)
  • Clinical IT System Administrators
  • Healthcare Integration Engineers
  • IT Managers and Project Managers in healthcare settings
  • Software Engineers and Developers specializing in healthcare IT
  • Other IT professionals with relevant experience in healthcare environments, as recognized by local, national, or European authorities

Each IT professional should possess the relevant academic degree, professional certification, or demonstrable experience that qualifies them for their role in the healthcare organization, in accordance with the requirements of the United States, Europe, or other regions where the device is provided.

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 categories that are visible.

Explainability

For visual signs that can be quantified in terms of count and extent, the underlying models not only calculate a final value, such as the number of lesions, but also determine their locations within the image. Consequently, the output for these visual signs is accompanied by additional data, which varies depending on whether the quantification involves count or extent.

  • Count. When a visual sign is quantifyed by counting, the device generates bounding boxes for each detected entity. These bounding boxes are defined by their x and y coordinates, as well as their height and width in pixels.
  • Extent. When a visual sign is quantifyed by its extent, the device outputs a mask. This mask, which is the same size as the image, consists of 0's for pixels where the visual sign is absent and 1's for pixels where it is present.

The explainability output can be found with the explainabilityMedia key. Here is an example:

{
  "explainabilityMedia": {
    "explainabilityMedia": {
      "content": "base 64 image",
      "detections": [
        {
          "confidence": 98,
          "label": "nodule",
          "p1": {
            "x": 202,
            "y": 101
          },
          "p2": {
            "x": 252,
            "y": 154
          }
        },
        {
          "confidence": 92,
          "label": "pustule",
          "p1": {
            "x": 130,
            "y": 194
          },
          "p2": {
            "x": 179,
            "y": 245
          }
        }
      ]
    }
  }
}

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
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  • Intended use or purpose
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.)