R-TF-015-008 Clinical development plan_2023_002

Clinical development plan

The following chart shows an overview of the process by which we provide evidence of the performance of our device.

Peer-reviewed publications

Automatic SCOring of Atopic Dermatitis Using Deep Learning: A Pilot Study

• Published: February 10, 2022
• DOI: https://doi.org/10.1016/j.xjidi.2022.100107

Dermatology Image Quality Assessment (DIQA): Artificial intelligence to ensure the clinical utility of images for remote consultations and clinical trials

• Published: December 13, 2022
• DOI: https://doi.org/10.1016/j.jaad.2022.11.002

Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A novel tool to assess the severity of hidradenitis suppurativa using artificial intelligence

• Published: 05 June 2023
• DOI: https://doi.org/10.1111/srt.13357

Automatic Urticaria Activity Score (AUAS): Deep Learning-based Automatic Hive Counting for Urticaria Severity Assessment

• Published: July 11, 2023
• DOI: https://doi.org/10.1016/j.xjidi.2023.100218

The Utility and Reliability of a Deep Learning Algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies

• Published: September 6, 2024
• DOI: https://doi.org/10.1007/s00405-024-08951-z

Upcoming papers

Automatic Psoriasis Area and Severity Index

• Code: APASI
• Current status: Review by the authors
• Journal: Journal of the American Academy of Dermatology (JAAD)
• Submission date: November, 2024

Severity assessment and characterisation of Pressure Ulcer using deep learning

• Code: WOUNDS
• Current status: Work in progress.
• Journal: Journal of the American Academy of Dermatology (JAAD)
• Submission date: Q1, 2025

Overcoming measurement challenges in clinical trials: a deep learning-based approach for monocular surface area measurement

• Code: AREA ESTIMATION
• Current status: Work in porgress. Waiting for feedback from co-authors.
• Journal: Journal of Investigative Dermatology (JID)
• Submission date: December, 2024

Development and assessment of an automated imaging medical device based on artificial intelligence for evaluating the severity of generalized pustular psoriasis

• Code: AGPPGA
• Current status: Under review by the authors.
• Journal: Journal of Investigative Dermatology Innovations
• Submission date: November, 2024

Enhancing diagnostic accuracy of generalised pustular psoriasis with Legit.Health, an AI-based certified medical device

• Code: DIAGNOSTIC SUPPORT
• Current status: Work in progress. Pending Review.
• Journal: Journal of Investigative Dermatology (JID) or Skin Research and Technology.
• Submission date: Q1, 2025

Enhanced Dermatology Image Quality Assessment Via Cross-Domain Training

• Code: DIQA V2
• Current status: Work in progress
• Journal: Image Vision and Computing
• Submission date: December, 2024

Limitations of the Diverse Dermatology Images dataset for fairness benchmarking of machine learning models in dermatology: findings and recommendations

• Code: FITZPATRICK
• Current status: Work in progress
• Journal: Pending determination of the journal
• Submission date: Q1, 2025

Performance Study of a Medical Device for Diagnostic Support with Artificial Intelligence in 15 Nursing Homes

• Code: SANITAS
• Current status: Work in progress. Paper in preparation.
• Journal: Pending determination of the journal
• Submission date: Q1, 2025

Enhancing diagnostic accuracy of melanoma with Legit.Health, an AI-based certified medical device

• Code: MELANOMA
• Current status: Analysis of data in progress
• Journal: Pending determination of the journal
• Submission date: Q1, 2025

Optimization of clinical flow in patients with dermatological conditions using Legit.Health's AI medical device

• Code: IDEI
• Current status: Project still to be finished
• Journal: Pending determination of the journal
• Submission date: Q2, 2025

Activity in medical congress

Experience of using the Legit.Health artificial intelligence tool in the digital monitoring of chronic dermatological pathologies

Automatic International Hidradenitis Suppurativa Severity Score System

Hidradenitis artificial intelligence pilot project

Analysis of the Performance of a Medical Device with Artificial Intelligence for the Optimization of Clinical Flow in Patients with Pigmented Lesions

Performance Study of a Diagnostic Support Medical Device with Artificial Intelligence in 15 Nursing Homes

Analysis of the Performance of a Medical Device with Artificial Intelligence for the Optimization of Clinical Flow in Patients with Pigmented Lesions

Performance Analysis of an AI-Enhanced Medical Device for Optimizing Clinical Workflow in Patients with Pigmented Lesions

Automatic Generalized Pustular Psoriasis Physician Global Assessment (AGPPGA)

Validation study of artificial intelligence for the detection of Melanoma

ALADIN scale for automatic measurement of acne severity

Double intelligence: the future telematic follow-up of patients by Dra Elena Sanchez-Largo

Artificial Intelligence for the optimization of the referral of patients with skin pathologies

Automatic calculation of urticaria with Artificial Intelligence for accurate counting of wheals

Optimization of teledermatology in primary care

Automatic SALT: Reducing variability in the measurement of alopecia

Clinical trials

Clinical validation study of a CAD system with artificial intelligence algorithms for early noninvasive detection of in vivo cutaneous melanoma.

• Code: LEGIT_MC_EVCDAO_2019
• Status: Finished
• Start date: November 22nd, 2019
• Finish date: April 10th, 2024
• Acceptance criteria:
  • AUC greater than 0.8
  • sensitivity of at least 80% or higher
  • specifity of at least 70% or higher

Pilot study for the clinical validation of a Computer Aided Diagnosis (CAD) system with artificial intelligence algorithms for a continuous and remote monitoring of the severity of the patient's condition in an objective and stable way.

• Code: LEGIT_COVIDX_EVCDAO_2022
• Status: Finished
• Start date: March 3rd, 2022
• Finish date: October 23rd, 2023
• Acceptance criteria:
  • A score of 8 or higher in the Clinical Utility Score (CUS) filled by the medical staff

Pilot study for the clinical validation of an artificial intelligence algorithm to optimize the appropriateness of dermatology referrals.

• Code: LEGIT.HEALTH_DAO_Derivación_O_2022
• Status: Ongoing
• Start date: April 7th, 2022
• Acceptance criteria:
  • Improve the adequacy of referrals to dermatology
  • A reduction of waiting lists (at least 30% Warshaw et al. 2011)
  • A reduction of the costs in secondary care

Optimization of clinical flow in patients with dermatological conditions using Artificial Intelligence.

• Code: LEGIT.HEALTH_IDEI_2023
• Status: First part of the study finished. Second part will start in Q1, 2025
• Start date: February 2nd, 2024
• Finish date: August 7th, 2024
• Acceptance criteria:
  • An improvement of diagnostic accuracy of 10% (Ferri et al. 2020)
  • Scores equal or greater than 70 on the System Usability Scale (SUS)
  • An AUC equal or greater than 0.8 detecting malignancy
  • A sensitivity of 80% detecting malignancy
  • A specifity of 70% detecting malignancy

Project to enhance Dermatology E-Consultations in Primary Care centres using Artifical Intelligence Tools.

• Code: LEGIT.HEALTH_PH_2024
• Status: Finished
• Start date: June 24th, 2022
• Finish date: January 10th, 2024
• Acceptance criteria:
  • An improvement of diagnostic accuracy of 10% (Ferri et al. 2020) in primary care physicians and dermatologists.

Non-Invasive Prospective Pilot in a Live Environment for the improvement of the diagnosis of skin pathologies in primary care and dermatology

• Code: LEGIT.HEALTH_SAN_2024
• Status: Finished
• Start date: November 15th, 2023
• Finish date: February 17th, 2024
• Acceptance criteria:
  • An improvement of diagnostic accuracy on both primary care physicians and dermatologists
  • A positive view of Legit.Health regarding diagnosis support
  • A reduction of 30% of referral to dermatology (Warshaw et al. 2011)
  • An improvement in remote consultations

Non-Invasive Prospective Pilot in a Live Environment for the improvement of the diagnosis of Generalized Pustular Psoriasis

• Code: LEGIT.HEALTH_BI_2024
• Status: Finished
• Start date: May 1st, 2024
• Finish date: August 29th, 2024
• Acceptance criteria:
  • An improvement of diagnosis accuracy of generalized pustular psoriasis (GPP)
  • An improvement of diagnosis accuracy of other skin conditions such as hidradenitis suppurativa or palmoplantar pustulosis on both, primary care physicians and dermatologists

Non-Invasive Prospective Pilot in a Live Environment for the improvement of the diagnosis of skin pathologies in primary care

• Code: LEGIT.HEALTH_PH_2024_NIPPLE
• Status: Finished
• Start date: June 4th, 2024
• Finish date: September 13th, 2024
• Acceptance criteria:
  • An improvement of diagnostic accuracy on both primary care physicians and dermatologists.
  • A reduction of 30% of referral to dermatology (Warshaw et al. 2011).
  • An improvement in remote consultations.

Planned studies

Study for the clinical validation of a medical device for the priorization of consultations in patients with suspected skin cancer

• Code: LEGIT.HEALTH_DAO_Derivation_St Pau_2024
• Status: Protocol completed. Pending Ethics Committee Approval
• Expected start date: November, 2024
• Objective: To evaluate the impact of the medical device Legit.Health on prioritizing dermatology follow-up consultations in patients with suspected melanoma
• Study design:
  • Type: Observational and prospective study with intervention
  • Sample size: 140 patients
  • Duration: 1 year

Evaluating the performance of Legit.Health in automated triage in teledermatology

• Code: LEGIT.HEALTH_triaje_VH_2024
• Status: Study under review by the Ethics Committee
• Expected start date: January, 2025
• Objective: To assess the impact of implementing Legit.Health in reducing the average waiting time for skin cancer patients and to assess the sensitivity and specifity of Legit.Health detecting malignancy
• Study design:
  • Type: Observational and retrospective study
  • Sample size: 30000 images will be analyzed in this study
  • Duration: 1 year

Pilot study for the validation of a medical device for optimizing the referrals from primary care to dermatology

• Code: LEGIT.HEALTH_DAO_Derivation_AR_2024
• Status: Protocol in draft
• Expected start date: February, 2025
• Objective: To validate the medical device Legit.Health to optimize the referral of patients with skin conditions from primary care to dermatology, and thereby reduce the waiting time for patients with serious conditions
• Study design:
  • Type: Observational and prospective study
  • Sample size: 1000 patients
  • Duration: 14 months

Pilot study for the clinical validation of a medical device for the automatic assessment of severity and remote monitoring of patients with acne

• Code: LEGIT.HEALTH_acne
• Status: Study under review by the Ethics Committee
• Expected start date: December, 2024
• Objective: To validate that the ALADIN severity scale developed by AI LABS GROUP S.L. measures the severity of facial acne with a capacity similar to or greater than that of a specialist, using a photograph taken with a smartphone
• Study design:
  • Type: Observational and prospective study
  • Sample size: 30 patients
  • Duration: 9 months

Pilot study for the clinical validation of the SALT automatic system for measuring the severity of alopecia areata based on artificial intelligence

• Code: LEGIT.HEALTH_aSALT
• Status: Protocol in draft
• Expected start date: December, 2024
• Objective: To validate that the aSALT severity measurement system for alopecia areata achieves an accuracy equal to or greater than that of an expert clinical using the "gold standard" SALT (Severity of Alopecia Tool)
• Study design:
  • Type: Observational and prospective-retrospective study
  • Sample size: 30 patients
  • Duration: 3 months

Pilot study for the clinical validation of an automatic EASI scoring system with artificial intelligence algorithms to assess the severity of atopic dermatitis

• Code: LEGIT_aEASI_2024
• Status: Study under review by the Ethics Committee
• Expected start date: November, 2024
• Objective: To validate an automatic measurement system of the Eczema Area and Severity Index (EASI) based on artifical intelligence to determine the severity of atopic dermatitis, and that it does so with an accuracy simlar or better than the consensus of experts who use the "gold standard" EASI -Study design:
  • Type: Observational and retrospective study
  • Sample size: 100 images of different patients
  • Duration: 3 months

Pilot study for the clinical validation of a medical device for the quantification of severity and monitoring of the evolution of patients with FFA (Frontal Fibrosing Alopecia)

• Code: LEGIT_AFF_EVCDAO
• Status: Protocol in draft
• Expected start date: Q2 2025
• Objective: To validate that the medical device Legit.Health is capable of measuring the severity of frontal fibrosing alopecia by automatically counting hairs and verify that it does so with a capacity equal or greater than the respective "gold standard" completed by the specialist
• Study design:
  • Type: Observational and prospective study
  • Sample size: 100 patients
  • Duration: 2 years

Timeline and milestones

• 2024
  • Start of the study LEGIT.HEALTH_DAO_Derivation_St Pau_2024
  • Start of the study LEGIT.HEALTH_acne
  • Start of the study LEGIT.HEALTH_aEASI_2024
  • Start of the study LEGIT.HEALTH_aSALT
• 2025
  • Start of the study LEGIT.HEALTH_triaje_VH_2024
  • Start of the study LEGIT.HEALTH_DAO_Derivation_AR_2024
  • Start of the study LEGIT_AFF_EVCDAO
  • Completition of the study LEGIT.HEALTH_DAO_Derivación_O_2022
  • Completition of the study LEGIT.HEALTH_DAO_Derivation_St Pau_2024
  • Completition of the study LEGIT.HEALTH_acne
  • Completition of the study LEGIT.HEALTH_aEASI_2024
  • Completition of the study LEGIT.HEALTH_aSALT
  • CE certification completed
  • FDA certification completed
  • Expected Market launch
• 2026
  • Completition of the study LEGIT.HEALTH_triaje_VH_2024
  • Completition of the study LEGIT.HEALTH_DAO_Derivation_AR_2024
• 2027
  • Completition of the study LEGIT_AFF_EVCDAO

Conclusions

This CDP outlines a structured and comprehensive approach to the clinical development of the device, ensuring thorough evaluation at each phase of development. The plan outlines a clear regulatory pathway, leading to the successful market introduction and long-term monitoring of the device.

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