1. A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss from Optic Disc Photographs

    A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss from Optic Disc Photographs

    Purpose To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch’s membrane opening (BMO-MRW) from spectral domain-optical coherence tomography (SDOCT). Design Cross-sectional study Methods 9,282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (20%). A DL convolutional neural network was trained to predict the SDOCT BMO-MRW global and sector values when evaluating optic disc photographs. The predictions of the DL network were compared to ...

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