Leveraging Machine Learning & Mobile Application Technology for Vitiligo Management: A Proof-of-Concept

Vitiligo, a dermatological condition characterized by depigmented patches on the skin, affects up to 2% of the global population. Its management is complex, often hindered by delayed diagnosis due to limited access to dermatologists and/ digital tools. Recent advancements in machine learning (ML) offer a potential solution by providing digital tools for early detection and management. This proof-of-concept study describes the development of a machine learning pipeline integrated into a mobile application for vitiligo assessment. Using a dataset of 1,309 images, including segmental and generalized vitiligo, the CNN was trained for binary classification with an accuracy of 95%. The model segments depigmented patches and conducts colorimetric analysis for precise evaluation. We compared traditional Woods lamp imaging with CNN-generated maps, showing comparable or superior results in detecting faint depigmentation. Developed using Flutter for cross-platform compatibility, the app enables patients to upload images for analysis and track disease progression. A Golang-based backend ensures robust data management, while a PostgreSQL database supports secure storage of patient information. The integration of Azure Active Directory enhances security and user authentication. This approach aims to bridge the gap in dermatological care by providing an accessible, ML-driven solution for vitiligo management. Future iterations will expand the applications capability to screen for other depigmentation disorders, incorporate automated scoring systems for more personalized patient management, and communication services.