Automated Measurement of Depigmentation Extent with a New AI Tool Applied to the Example of Vitiligo.

INTRODUCTION: We 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).

METHODS: Our convolutional neural network ("UNet") uses encoder-decoder architecture to process photographic images and quantify areas of skin affected by vitiligo.

RESULTS: We 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.

CONCLUSION: We found that using a UNet to segment vitiligo lesions can allow us to digitize clinically meaningful measures for vitiligo.

TRIAL REGISTRATION: The phase 2b study: NCT03715829.