An OCT-trained deep learning algorithm’s predictions of retinal nerve fiber layer thicknesses from fundus photographs effectively predicted future development of visual field defects in eyes of glaucoma suspects.
Researchers in a retrospective cohort study used a machine-to-machine (M2M) OCT-trained deep learning algorithm to predict retinal nerve fiber layer (RNFL) thickness measurements from fundus photographs. The model was applied to fundus photographs of glaucoma suspect eyes followed over time. Researchers investigated the efficacy of using the M2M-predicted RNFL thicknesses to
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