November 30, 2025

Quantitative Assessment of Basement Membrane Loss in Melasma Using Attenuation Coefficient Estimation Based on Optical Coherence Tomography

Key Points

Melasma shows significant basement membrane loss, with a measured reduction of 66.0% compared to perilesional skin.
The proposed deep learning model achieves 81.4% accuracy and enhances image contrast for better segmentation.
Assessment utilizes optical coherence tomography and attenuation coefficient mapping to evaluate histopathology noninvasively.
These findings support the potential clinical application of the method for monitoring treatment effects such as tranexamic acid.

Abstract

ABSTRACT Melasma is a common pigmentary disorder involving melanin deposition and structural alterations. Current diagnostic methods mainly target pigmentation and lack real‐time assessment of histopathology. This study proposes a noninvasive, quantitative evaluation of basement membrane (BM) disruption in melasma using optical coherence tomography (OCT) with deep learning. Cross‐sectional skin images were generated by attenuation coefficient (AC) mapping of OCT B‐scans. An improved Unet (Res‐Att‐Unet), integrating residual and attention modules, was developed for BM segmentation. AC mapping enhanced image contrast, yielding superior BM segmentation over conventional OCT. The proposed model achieved an accuracy of 81.4%, F 1‐score of 83.8%, and IoU of 72.1%. BM loss in melasma (66.0% ± 19.8%) was significantly higher than in perilesional skin (47.2% ± 18.5%, p < 0.001). Longitudinal monitoring revealed a significant BM recovery after tranexamic acid (TXA) treatment. These results indicate that our proposed method can be potentially used in clinic for in vivo BM assessment, aiding in melasma diagnosis.

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