Objective and quantitative assessment of facial vitiligo via artificial intelligence-powered three-dimensional...

Traditional vitiligo assessment methods rely on subjective evaluation and two-dimensional photographic analysis, limiting consistency and accuracy in clinical practice.

To develop and validate an artificial intelligence (AI)-assisted, three-dimensional (3D) reconstruction-based system to accurately and objectively quantify facial vitiligo lesions.

In this diagnostic study, standardized three-view clinical facial photographs from patients with facial vitiligo seen at a tertiary referral centre between 2022 and 2024 were retrospectively analysed. After excluding incomplete or poor-quality images, 329 patients were included. A deep learning segmentation model was trained to detect depigmented lesions and applied to 3D reconstructions with surface mapping to assess facial curvature. Performance was compared with manual assessments made by board-certified dermatologists, with and without AI guidance, across two sessions.

The AI segmentation model achieved consistently high boundary delineation (F1 score > 0.98) and robust lesion identification accuracy (median Dice similarity coefficient approximately 0.85). When integrated into dermatologists' evaluations, AI assistance markedly reduced mean absolute error in lesion quantification (3.13 vs. 6.71) compared with manual assessment alone. Correlation with reference lesion areas also showed a higher Pearson correlation coefficient (r = 0.92 vs. 0.84), indicating enhanced reliability. Bland-Altman analysis further revealed reduced systematic bias and narrower limits of agreement, supporting the reproducibility of AI-assisted quantification. Collectively, these findings indicate that AI guidance not only improves segmentation performance but also augments the accuracy and consistency of clinical interpretation.

This study demonstrates the potential clinical impact of AI-assisted 3D reconstruction. The system provides precise and standardized quantification of facial vitiligo lesions by incorporating surface curvature into lesion mapping. Compared with manual assessments, AI-based support improved diagnostic accuracy, reduced interobserver variability and offered consistent measurements across sessions. These findings highlight the promise of integrating AI-driven 3D tools into routine dermatological practice to enhance patient monitoring and optimize treatment planning.