ArticleExperimental dermatology2026
Quantitative CRFF-OCT Imaging Features for Characterization of Disease Activity in Non-Segmental Vitiligo: A Machine Learning-Assisted Study.
Article in Experimental dermatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Vitiligo is an autoimmune pigmentary disorder characterized by progressive melanocyte loss and unpredictable activity. Objective of disease activity remains challenging, particularly in lesions with subtle clinical changes Cellular-resolution full-field optical coherence tomography (CRFF-OCT) enables non-invasive, high-resolution, histology-like visualization of skin microstructure. This study evaluated the feasibility of integrating CRFF-OCT with machine learning-assisted quantitative analysis for imaging-based characterization of active and stable vitiligo lesions. Fifty patients with non-segmental vitiligo were prospectively enrolled (2021-2022). CRFF-OCT imaging was performed on lesional, perilesional, and normal-appearing skin within the same anatomical region. Quantitative features describing epidermal structure, dermal-epidermal junction (DEJ) morphology, and pigment-associated reflectivity were extracted. A machine learning-assisted computer-aided detection (CADe) framework incorporating 13 features was developed for image-level classification of lesion activity. CRFF-OCT imaging demonstrated distinct microstructural patterns between active and stable lesions. Basal epidermal pigment-associated reflectivity was significantly lower in stable lesions compared with active lesions (9.94% ± 10.06% vs. 21.84% ± 11.08%), with corresponding differences in lesion-to-normal reflectivity ratios (0.21 vs. 0.56). Quantitative analysis revealed significant differences in epidermal thickness, DEJ associated reflectivity, inter-layer contrast, and reflectivity heterogeneity. Among the 13 extracted features, 9 differed significantly between groups and were incorporated into classification models. The CADe framework achieved a maximum image-level classification accuracy of 80.6% using a support vector machine model. These findings demonstrate the feasibility of CRFF-OCT-based quantitative imaging for objective characterization of vitiligo lesion status and support its potential role in disease activity assessment and longitudinal monitoring.
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