AI-based Medical Device Validation for Early Melanoma Detection

NCT06221397 | Completed Results

• 1 other identifier: LEGIT_MC_EVCDAO_2019
• Study Type: observational
• Target: 105
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to learn if a computer-aided diagnosis (CAD) system can help identify skin cancer (cutaneous melanoma). The research focuses on adults who have skin spots that a doctor thinks might be cancerous. The main questions the study aims to answer are: Can the artificial intelligence (AI) tool accurately identify melanoma in skin images? How does the tool's accuracy compare to the clinical judgment of expert skin doctors (dermatologists)? Researchers will compare the results from the AI tool to the final diagnosis made by doctors or through a skin biopsy. A biopsy is a medical test where a small piece of skin is removed and checked in a lab. Participants will: Have their skin spots photographed using a special camera attached to a smartphone. Allow researchers to use their clinical data and biopsy results for the study. The study does not change the medical care participants receive. Doctors will continue to treat participants as they normally would. By testing this tool, researchers hope to find a way to detect skin cancer earlier and more accurately

Conditions

Melanoma; Melanoma, Skin

Keywords

melanoma; primary care; dermatology; diagnosis; Artificial Intelligence; Severity

Outcome Measures

Primary Outcomes (4)

• Area Under the ROC Curve (AUC) for Melanoma Detection

  Measures the device's ability to distinguish between melanoma and non-melanoma cases using predicted probabilities.

  At the time of the single clinical visit (Baseline).

• Accuracy for Melanoma Detection

  Accuracy represents the percentage of all cases where the AI software's primary (top-ranked) prediction correctly matched the confirmed medical diagnosis. The "confirmed diagnosis" was determined by either a laboratory biopsy (the gold standard) or a consensus of expert dermatologists. To calculate this, the AI analyzed high-resolution dermoscopic images of skin lesions. The software succeeded if its highest-probability diagnosis category matched the actual disease category of the lesion. Only images meeting a minimum visual quality score (DIQA ≥ 5) were included in this analysis to ensure the results reflect performance in a professional clinical setting.

  At the time of the single clinical visit (Baseline)

• Sensitivity for Melanoma Detection

  The percentage of true positive melanoma cases correctly identified by the device.

  At the time of the single clinical visit (Baseline).

• Specificity for Melanoma Detection

  The percentage of true negative (benign) cases correctly identified by the device.

  At the time of the single clinical visit (Baseline).

Secondary Outcomes (7)

• Top-1 Accuracy for Multiple ICD Categories

  At the time of the single clinical visit (Baseline).

• Top-3 Accuracy for Multiple ICD Categories

  At the time of the single clinical visit (Baseline).

• Top-5 Accuracy for Multiple ICD Categories

  At the time of the single clinical visit (Baseline).

• Area Under the ROC Curve (AUC) for Malignancy Detection

  At the time of the single clinical visit (Baseline).

• Sensitivity for Multiple Malignant Conditions Detection

  At the time of the single clinical visit (Baseline).

Study Arms (1)

Patients with suspected cutaneous malignancy

Group/Cohort Description The study group consists of adult patients (over 18 years old) who presented at the Dermatology Departments of Hospital Universitario Cruces and Hospital Universitario Basurto with skin lesions suspected of being malignant. As this is an observational study, participants were not assigned to any new medical interventions, drugs, or treatments as part of the research protocol.

Device: AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.

Interventions

AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis. DEVICE

The intervention is a software-only medical device that utilizes artificial intelligence and machine vision algorithms to analyze digital images of the skin. Unlike traditional diagnostic tools, this system is designed to provide quantitative data on visible clinical signs and an interpretative distribution of possible disease categories (ICD codes). Key Distinguishing Features Non-Invasive Diagnostic Support: It acts as a clinical decision-support tool to help practitioners prioritize patients based on malignancy risk, rather than providing a standalone or confirmatory diagnosis. Broad ICD Recognition: While many tools focus only on melanoma, this system is capable of recognizing a variety of ICD categories, including basal cell carcinoma, nevi, and dermatofibroma Advanced Image Preprocessing: The system includes a Dermatology Image Quality Assessment (DIQA) algorithm to ensure images have sufficient visual quality before analysis.

Patients with suspected cutaneous malignancy

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Hospital Universitario Cruces and Hospital Universitario Basurto.

You may qualify if:

• Patients with skin lesions with suspected malignancy
• Age over 18 years old
• Patients who consent to participate in the study by signing the Informed Consent form

You may not qualify if:

• Patients under 18 years of age

Sponsors & Collaborators

• AI Labs Group S.L: lead
• Servicio Vasco de Salud Osakidetza, Spain: collaborator
• Osakidetza: collaborator
• Hospital de Basurto: collaborator
• Hospital de Cruces: collaborator

Study Sites (1)

University Hospital of Cruces

Barakaldo, Biscay, 48903, Spain

Location

MeSH Terms

Conditions

Melanoma; Disease

Interventions

Diagnosis, Computer-Assisted

Condition Hierarchy (Ancestors)

Neuroendocrine Tumors; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Nerve Tissue; Nevi and Melanomas; Skin Neoplasms; Neoplasms by Site; Skin Diseases; Skin and Connective Tissue Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Diagnosis

Limitations and Caveats

The study's primary limitation was a smaller final sample size (105 participants) compared to the initial target (200), primarily due to the COVID-19 pandemic's impact on clinical recruitment. Additionally, the reliance on a specific hardware setup (DermLite Foto X and smartphones) means results may not generalize to all photographic equipment. Lastly, the exclusion of low-quality images (DIQA \< 5) implies the AI's performance depends on the user's ability to capture clear photos.

Results Point of Contact

• Title: Dr. Jordi Barrachina - Clinical Affairs Manager
• Organization: AI Labs Group S.L.

jordibarrachina@legit.health

+34 653 08 83 37

Study Officials

• Jesús Gardeazabal, PhD

  Hospital de Cruces

  PRINCIPAL INVESTIGATOR

• Rosa María Ize, PhD

  Hospital Universitario Basurto

  PRINCIPAL INVESTIGATOR

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Day
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 15, 2024
• First Posted: January 24, 2024
• Study Start: September 17, 2020
• Primary Completion: November 13, 2023
• Study Completion: November 13, 2023
• Last Updated: March 16, 2026
• Results First Posted: March 16, 2026

Record last verified: 2026-02

Locations

ES (1)