NCT06221397

AI-based Medical Device Validation for Early Melanoma Detection

Clinical Validation Study of a CAD System With Artificial Intelligence Algorithms for Early Noninvasive in Vivo Cutaneous Melanoma Detection

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 105 (actual)
• Sponsor: AI Labs Group S.L · Industry
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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

Detailed description

This study is designed to clinically validate a computer-aided diagnosis (CAD) system that utilizes artificial intelligence (AI) and machine vision to assist in the detection of cutaneous melanoma in its early stages. Cutaneous melanoma is a form of skin cancer that is treatable when identified early; however, differentiating early melanoma from benign skin lesions during visual examination presents a challenge for healthcare professionals. Study Design and Methodology The research is a prospective, observational, and cross-sectional study conducted at Hospital Universitario Cruces and Hospital Universitario Basurto in Spain. The protocol evaluates the diagnostic performance of an AI device using clinical images without interfering with routine patient care. * Participant Selection: The study focuses on adults with skin lesions suspected of malignancy during regular clinical visits. * Image Acquisition: Researchers capture photographs of skin lesions using a smartphone equipped with a specialized dermoscopic camera. * Data Collection: Clinical and demographic data, such as age and sex, are collected alongside the digital images. * Preprocessing: All images undergo a standardized preprocessing step where the lesion is cropped to minimize background noise for the algorithm. * AI Analysis: The system processes the cropped images to generate a malignancy probability and a list of possible disease categories. * Reference Standard: AI predictions are compared against a composite "Gold Standard." This standard is based on pathological anatomy results from a biopsy or, where a biopsy is not clinically indicated, the consensus diagnosis of expert dermatologists with extensive clinical experience. Study Phases and Sample Size Plan The investigation was planned in two phases to ensure a representative dataset: * Initial Phase: An initial pilot phase to assess the preliminary workflow. * Extension Phase: A second phase intended to broaden the sample to include a wider range of benign lesions, such as nevi, to better reflect the distribution of cases seen in daily clinical practice. * Sample Size Target: The study was designed to recruit participants until a sufficient number of confirmed melanoma cases were achieved to provide the statistical power required to validate the device's performance metrics. Performance Evaluation Measures The device's effectiveness is evaluated through the following pre-specified statistical metrics: * Primary Metrics: Area Under the ROC Curve (AUC), sensitivity, and specificity for the detection of melanoma. * Secondary Metrics: Top-K accuracy (evaluating if the correct diagnosis is within the top 1, 3, or 5 predictions) and malignancy prediction values, including Positive Predictive Value (PPV) and Negative Predictive Value (NPV). * Safety Assessment: The protocol includes the systematic recording of any adverse events or product deficiencies encountered during the use of the device to evaluate its safety profile as a clinical decision-support tool.

Conditions

• Melanoma
• Melanoma, Skin

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2020-09-17
• Primary completion: 2023-11-13
• Completion: 2023-11-13
• First posted: 2024-01-24
• Last updated: 2026-03-16
• Results posted: 2026-03-16

Locations

1 site across 1 country: Spain