Deep Learning-Based Vitiligo Activity Evaluation Using Wood’s Lamp Imaging: A Clinical Decision Support

Abstract

Vitiligo is an autoimmune disorder characterized by heterogeneous and unpredictable depigmentation, which poses substantial challenges for objective disease monitoring and treatment evaluation in routine clinical practice. To address the lack of automated and quantitative follow-up tools, we designed and validated an end-to-end deep learning–based system using Wood’s lamp imaging to support lesion localization, longitudinal tracking, and activity assessment. The framework employs Mask R-CNN for automated lesion detection and segmentation, followed by quantitative pigmentation-state analysis and disease activity evaluation using t-distributed stochastic neighbor embedding (t-SNE) and the Vitiligo Disease Activity (VIDA) score. Disease progression is further characterized through risk, correlation, and survival analyses. Quantitatively, the Mask R-CNN model achieved robust performance, with a Dice coefficient of 90.5%, a mean intersection-over-union of 83.1%, and an area under the curve of 0.9567 on external validation, supporting reliable lesion delineation under Wood’s lamp imaging. When benchmarked against representative segmentation backbones (U-Net, U-Net++, U2-Net, and DeepLabV3), the Mask R-CNN–based pipeline yielded the highest mean IoU (83.1 ± 3.9% ) while maintaining a competitive Dice score (90.5 ± 1.38% ). t-SNE analysis effectively separated pigmentation states, revealing distinct patterns of hyperpigmentation, hypopigmentation, and apigmentation that were consistent with clinical activity. Longitudinal survival analysis demonstrated that stable lesions derived significant benefit from sustained treatment, whereas active lesions exhibited elevated early risk. Age-stratified analysis identified the highest relapse risk in patients aged 11–20 years, while patients aged 71–80 years showed reduced disease stability. Regression analysis further indicated that, during the stable phase, increased hypopigmentation was associated with subsequent pigment regeneration, underscoring stage-dependent treatment effects. Integrating deep learning–based lesion analysis with clinically grounded activity and longitudinal risk modeling enables precise, objective monitoring of vitiligo and supports personalized, stage-aware treatment decision-making.