Quantitative Assessment of Basement Membrane Loss in Melasma Using Attenuation Coefficient Estimation Based on...

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%, F1-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.