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Introduction <p>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).</p> Methods <p>Our convolutional neural network (“UNet”) uses encoder-decoder architecture to process photographic images and quantify areas of skin affected by vitiligo.</p> Results <p>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.</p> Conclusion <p>We found that using a UNet to segment vitiligo lesions can allow us to digitize clinically meaningful measures for vitiligo.</p> Trial Registration <p>The phase&#xa0;2b study: NCT03715829.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Yalei Chen,
  • Tatjana Lukic,
  • All-Shine Chen,
  • Roni Adiri,
  • Helen Tran,
  • Gregor Schaefer,
  • Pranab Ghosh,
  • Subha Madhavan,
  • Koshika Soma,
  • Margaret Gamalo

摘要

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.