Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects

摘要

PurposeExisting summary statistics based upon optical coherence tomography (OCT) scans and/or visual fields (VF) are suboptimal for distinguishing between healthy and glaucomatous eyes in the clinic. This study evaluates the extent to which a hybrid deep learning method (HDLM), combined with a single wide-field OCT protocol, can distinguish eyes previously classified as either healthy suspects or mild glaucoma.