AI-based Medical Device Validation for Early Melanoma Detection

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.