Automated diagnosis and staging of Fuchs’endothelial cell corneal dystrophy using deep learning

Background:To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs’endothelial corneal dystrophy(FECD)without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography(HD-OCT).Methods:In this observational case-control study,104 eyes(53 FECD eyes and 51 healthy controls)received HDOCT imaging(Envisu R2210,Bioptigen,Buffalo Grove,IL,USA)using a 6 mm radial scan pattern centered on the corneal vertex.FECD was clinically categorized into early(without corneal edema)and late-stage(with corneal edema).A total of 18,720 anterior segment optical coherence tomography(AS-OCT)images(9180 healthy;5400 early-stage FECD;4140 late-stage FECD)of 104 eyes(81 patients)were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema.Using 5-fold cross-validation on the dataset containing 11,340 OCT images(63 eyes),the network was trained with 80%of these images(3420 healthy;3060 early-stage FECD;2700 late-stage FECD),then tested with 20%(720 healthy;720 early-stage FECD;720 late-stage FECD).Thereafter,a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-OCT scans of 41 eyes(5040 healthy;1620 early-stage FECD 720 late-stage FECD).Visualization of learned features was done,and area under curve(AUC),specificity,and sensitivity of the prediction outputs for healthy,early and late-stage FECD were computed.Results:The final model achieved an AUC of 0.997±0.005 with 91%sensitivity and 97%specificity in detecting early-FECD;an AUC of 0.974±0.005 with a specificity of 92%and a sensitivity up to 100%in detecting late-stage FECD;and an AUC of 0.998±0.001 with a specificity 98%and a sensitivity of 99%in discriminating healthy corneas from all FECD.Conclusion:Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.