1. Hybrid Deep Learning on Single Wide-field Optical Coherence Tomography Scans Accurately Classifies Glaucoma Suspects

    Hybrid Deep Learning on Single Wide-field Optical Coherence Tomography Scans Accurately Classifies Glaucoma Suspects

    Purpose: Existing summary statistics based upon optical coherence tomographic (OCT) scans and/or visual fields (VFs) 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. Methods: In total, 102 eyes from 102 patients, with or suspected open-angle glaucoma, had previously been classified by 2 glaucoma experts as either glaucomatous (57 eyes) or healthy/suspects (45 eyes). The HDLM had access only to information from a ...

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