In American journal of ophthalmology ; h5-index 67.0
PURPOSE : To report a multi-disease deep-learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs' endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images.
STUDY DESIGN : Development of a deep learning neural network diagnosis algorithm METHODS: A total of 158,220 AS-OCT images from 879 eyes of 478 subjects were used to develop and validate a classification deep network. After a quality check, the network was trained and validated using 134,460 images. We tested the network using a test set of consecutive patients involving 23,760 AS-OCT images of 132 eyes of 69 patients. The area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1-score and 95% confidence intervals (CI) were computed.
RESULTS : The MDDN achieved eye-level AUROCs > 0.99 (95% CI: 0.90, 1.0), AUPRCs > 0.96 (95% CI: 0.90, 1.0), and F1-scores > 0.90 (95% CI: 0.81, 1.0) for DES, FED, and KCN, respectively.
CONCLUSIONS : MDDN is a novel diagnostic tool for corneal diseases that can be used to automatically diagnose KCN, FED, DES using only AS-OCT images.
Elsawy Amr, Eleiwa Taher, Chase Collin, Ozcan Eyup, Tolba Mohamed, Feuer William, Abdel-Mottaleb Mohamed, Shousha Mohamed Abou
Corneal diseases, Diagnosis, OCT Imaging