Clinical Research Directory

AI in MF Diagnosis

The aim of this observational study is to evaluate the diagnostic performance of an AI algorithm in the histopathological diagnosis of MF compared to certified dermatopathologists.

Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study

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.