Precision measurement of stratum corneum thickness in OCT images using variational autoencoders and advanced DSP techniques

skin imaging; however, reliable and clinically meaningful quantification of stratum corneum (SC) thickness remains challenging. This review summarizes 2 decades of methodological evolution, highlighting the transition from early manual and rule-based approaches to modern deep learning-driven segmentation strategies. Particular emphasis is placed on recent hybrid frameworks that integrate physics-informed digital signal processing with generative deep learning models, which collectively improve boundary detection robustness, reduce annotation dependency, and enhance model interpretability. These advances have significantly expanded the clinical utility of OCT-based SC assessment, enabling more sensitive disease monitoring, improved evaluation of therapeutic and cosmetic interventions, and broader applications in dermatologic diagnostics. Finally, we outline emerging opportunities for real-time, marker-free analysis, multimodal data fusion, and the development of explainable and generalizable algorithms to support precision and personalized dermatologic care.