Imaging and machine learning reveal pigment ring spatial characteristics in vitiligo progression.

BACKGROUND

Vitiligo is a chronic autoimmune disorder causing skin depigmentation due to melanocyte loss, with pigment ring dynamics being key to understanding and managing the disease.

METHODS

Data from 105 vitiligo patients were collected using reflectance confocal microscopy (RCM) imaging to analyze pigment ring patterns across progressive and stable stages. A multi-task self-supervised learning framework was used to identify and classify pigment ring types (complete, depressed, incomplete, and absent). Graph-based structural analysis was applied to characterize the spatial and network dynamics of pigment rings, incorporating metrics such as clustering coefficients, centrality measures, and Ripley's statistics.

RESULTS

Progressive-stage patients exhibited denser clustering and spatial propagation of absent and depressed pigment rings, which played dominant roles in the network. Stable-stage patients showed higher clustering coefficients for complete rings, indicating restored structural stability. The learning framework demonstrated high accuracy in identifying pigment ring types, with an AUC of 0.99 and PR score of 0.95. Ripley's K-function analysis highlighted significant clustering of absent and depressed rings in the progression stage, correlating with active disease.

CONCLUSION

Integrating imaging, machine learning, and graph-based analysis offers key insights into vitiligo progression, enabling better staging and personalized treatments, with future research needed for broader validation and model refinement.