A novel single-cell level evaluation method for corneal endothelial cell function.

Approximately 10 million people worldwide suffer from corneal diseases, with endothelial dysfunction being a leading cause of blindness. Current morphological assessments lack sensitivity for detecting early corneal endothelial abnormalities. Here, we present a novel diagnostic framework that evaluates endothelial cell function at the single-cell level by analyzing morphological alterations. Using machine learning, we developed an image-based recognition system to digitize cellular features. By applying geometric and mathematical principles, we established the "Xin-Value" (XV) as a new functional metric. In several corneal endothelial injury models, XV strongly correlated with mitochondrial function and stress markers, confirming its biological relevance. Leveraging XV, we refined a grading system for endothelial damage, demonstrating improved accuracy across clinicians of varying expertise. This study introduces a paradigm shift in corneal endothelial assessment, enabling highly sensitive, image-based detection of early dysfunction at the single-cell level, with potential applications in clinical screening and disease monitoring.