1. Deep Learning Approaches Predict Glaucomatous Visual Field Damage from Optical Coherence Tomography Optic Nerve Head Enface Images and Retinal Nerve Fiber Layer Thickness Maps

    Deep Learning Approaches Predict Glaucomatous Visual Field Damage from Optical Coherence Tomography Optic Nerve Head Enface Images and Retinal Nerve Fiber Layer Thickness Maps

    Purpose To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain optical coherence tomography (SDOCT) optic nerve head images. Design Evaluation of a diagnostic technology Participants 9,765 visual field (VF)–SDOCT pairs collected from 1,194 participants with and without GVFD (1909 eyes). Methods Deep learning models were trained to use SDOCT retinal nerve fiber layer (RNFL) thickness maps, RNFL enface images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation ...

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