aceArtificial Intelligence Techniques for Diagnosing and Quantifying Vitiligo: A Systematic Review and Meta-Analysis
The utilization of machine learning (ML) and deep learning (DL) for vitiligo, particularly in image and object recognition, allows for the creation of innovative techniques that may match the precision and accuracy of current clinical methodologies. This systematic review seeks to explore how imaging-based artificial intelligence (AI) models may improve the accuracy of vitiligo diagnosis, severity assessment, and monitoring. A comprehensive literature search was conducted across PubMed, IEEE Xplore, EMBASE, Comp…
Across 54 studies, imaging-based AI models detected vitiligo with an AUC above 0.90 and segmented lesions with a Dice score of 0.84, often matching human experts.
The authors state that larger, more diverse publicly available vitiligo datasets and full-body image segmentation studies are needed before full clinical integration.
Evidence
- Peer-reviewedJournal of Investigative Dermatology2026-10-05
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Truvace Impact Record TRV-2026-1857, v1: “Artificial Intelligence Techniques for Diagnosing and Quantifying Vitiligo: A Systematic Review and Meta-Analysis.” Truvace, 2026-10-18. /record/TRV-2026-1857 (accessed at citation time). sha256 ae9ca2152d57f02c…
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