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

Abstract 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 Wood’s 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. Competing Interest Statement The authors have declared no competing interest. Funding Statement N/A Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics approval for protocol 2021-144 : "Building Algorithms for classifying and tracking Skin Abnormalities"- National Research Council, Canada Clinician-Initiated Study, Principal Investigator, Dr. Colin Hong, Skinopathy Research. 302 Sheppard West, Toronto I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present work are contained in the manuscript