AI-based Medical Device Validation for Early Melanoma Detection

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

Interventions

DEVICE

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

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.

Sponsors & Collaborators

• Servicio Vasco de Salud Osakidetza, Spain

  collaborator UNKNOWN

• Osakidetza

  collaborator OTHER

• Hospital de Basurto

  collaborator OTHER

• Hospital de Cruces

  collaborator OTHER

• AI Labs Group S.L

  lead INDUSTRY

Principal Investigators

• Jesús Gardeazabal, PhD · Hospital de Cruces

• Rosa María Ize, PhD · Hospital Universitario Basurto

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2020-09-17

Primary Completion

2023-11-13

Completion

2023-11-13

Countries

• Spain

Study Locations