Cutaneous lymphoproliferative diseases (CLPDs) are a group of skin disorders that range from benign conditions, such as pseudolymphomas, to malignant forms like cutaneous T-cell and B-cell lymphomas. Mycosis fungoides is the most common malignant type, but diagnosis is often difficult because many benign skin conditions can mimic lymphoma. Current diagnostic methods rely on microscopic examination of biopsies, which can be subjective and vary between pathologists. This study aims to develop and validate a deep learning model that uses digitized biopsy images and clinical data to distinguish malignant CLPDs from benign ones. By applying artificial intelligence to dermatopathology, the project seeks to improve diagnostic accuracy, reduce variability, and support clinicians in making timely treatment decisions. The novelty of this work lies in applying advanced AI methods to a rare and challenging group of skin diseases, with the potential to enhance patient care in both specialized centers and resource-limited settings.
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See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Diagnostic accuracy of AI model
Timeframe: Baseline (In retrospective diagnostic studies, the moment the AI evaluates the historical slide is considered the patients's baseline).