An Open-Source Deep Learning Framework for Automated Corneal Segmentation in Anterior Segment Optical Coherence Tomography With Cross-Device External Validation

PURPOSE: To develop a deep learning model-Cornea nnU-Net Extractor (CUNEX)-for full-thickness corneal segmentation of anterior segment optical coherence tomography (AS-OCT) images and evaluate its utility in artificial intelligence research. METHODS: CUNEX was developed and evaluated using our Moorfields AS-OCT dataset of 194,599 scans from 37,499 patients, from which 100 normal, 100 keratoconus, and 100 Fuchs endothelial corneal dystrophy eyes were selected for model training. External validation was performed without fine-tuning using AS-OCT images acquired on 4 different OCT devices: Casia SS-1000, Anterion, Visante, and a ultrahigh-resolution custom build. CUNEX was benchmarked against 2 established models, CorneaNet and ScLNet. We then evaluated whether CUNEX-based segmentation influenced downstream deep learning classification of age, sex, and disease staging. RESULTS: On the internal MS-39 validation set, CUNEX achieved Dice similarity coefficient (DSC) and intersection over union (IoU) scores of 94% to 95% and 88% to 91% across healthy, keratoconus, and Fuchs endothelial corneal dystrophy eyes, comparable to ScLNet (within 3%) and superior to CorneaNet (8%-35% lower). On external datasets, CUNEX was the only model to produce anatomically meaningful segmentations across devices (DSC and IoU >70%), whereas ScLNet and CorneaNet showed substantial performance degradation (DSC and IoU