ArticleDermatology and therapy2026
Automated Measurement of Depigmentation Extent with a New AI Tool Applied to the Example of Vitiligo.
Article in Dermatology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03715829 (A PHASE 2B RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED, MULTICENTER, DOSE-RANGING STUDY TO EVALUATE THE EFFICACY AND SAFETY PROFILE OF PF-06651600 WITH A PARTIALLY BLINDED EXTENSION PERIOD TO EVALUATE THE EFFICACY AND SAFETY OF PF-06651600 AND PF-06700841 IN SUBJECTS WITH ACTIVE NON-SEGMENTAL VITILIGO), which is not on this map. Not yet cited in PubMed.
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A phase 2b randomized, double-blind, placebo-controlled, multicenter, dose-ranging study to evaluate the efficacy and safety profile of pf-06651600 with a partially blinded extension period to evaluate the efficacy and safety of pf-06651600 and pf-06700841 in subjects with active non-segmental vitiligo
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Abstract
introductionWe have developed a digital algorithm to assess skin pigmentation, specifically an artificial intelligence-based image analysis tool that segments photographed lesions and then scores them by Facial Vitiligo Area Scoring Index (F-VASI), in place of trained site investigators. Vitiligo, the disease used in this exemplary demonstration of the algorithm, is a chronic, acquired, immune-mediated depigmentation disease characterized by white macules and/or patches of skin. The F-VASI is a clinician-reported outcome that relies on manual assessment of affected body surface area (BSA) and level of depigmentation and is subject to inter- and intra-rater variability. Here, we present automated medical image segmentation of vitiligo lesions and digitization of validated scores, including F-VASI, BSA, and percentage of depigmentation (%Depigmentation).
methodsOur convolutional neural network ("UNet") uses encoder-decoder architecture to process photographic images and quantify areas of skin affected by vitiligo.
resultsWe trained and validated our model using cross-polarized participant photos from clinical trials, achieving 81% accuracy when predicting vitiligo lesions in new photos. In addition, we created an algorithm to digitize F-VASI assessment using estimates of BSA and %Depigmentation that were calculated using the predicted lesions in the photos. We were able to achieve an interclass correlation coefficient of 0.91 when comparing our digital F-VASI score to the manually estimated F-VASI score.
conclusionWe found that using a UNet to segment vitiligo lesions can allow us to digitize clinically meaningful measures for vitiligo.
trial registrationThe phase 2b study: NCT03715829.
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