Abstract:  Objective  To investigate the feasibility and clinical value of applying artificial intelligence for severity grading of Fuchs endothelial corneal dystrophy (FECD). Design Cross-sectional study. Participants  245 patients with FECD who visited the Ophthalmology Clinic of Peking University Third Hospital between January 2022 and June 2025. Method A total of 605 confocal microscopy images from FECD patients were collected and screened. The boundaries of guttae were manually annotated to establish the gold standard. A U-Net++ neural network combined with a watershed algorithm as post-processing was developed and tested. Nine neural networks, including U-Net, were constructed as control models. Main Outcome Measures The segmentation accuracy was evaluated using accuracy, sensitivity, specificity, Intersection over Union (IoU), Dice coefficient, and modified Hausdorff distance (MHD). The accuracy of the guttae parameters (area ratio, density, and average size) output by the model was also assessed. Results Among 605 confocal microscope images of 245  patients, the U-Net++ model achieved a Dice coefficient of 0.7676, IoU of 0.6229, accuracy of 0.9670, sensitivity of 0.7923, specificity of 0.9799, MHD of 33.99 px, and AUC of 0.9758. The mean relative errors for guttae area ratio, density, and average size were 0.453, 0.280, and 0.222, respectively. Overall, these results outperformed multiple control models. Conclusions The deep learning model on confocal microscope images can effectively detect lesion regions in FECD and output multiple guttae parameters for automated severity grading, contributing to the advancement of precise diagnosis and management of FECD.

Key words: Fuchs endothelial corneal dystrophy, Grading, Artificial intelligence, Deep learning