1. Deep Learning Assisted Detection of Glaucoma Progression in Spectral-Domain Optical Coherence Tomography

    Deep Learning Assisted Detection of Glaucoma Progression in Spectral-Domain Optical Coherence Tomography

    Purpose: To develop and validate a deep learning (DL) model for detection of glaucoma progression using spectral-domain optical coherence tomography (SDOCT) measurements of retinal nerve fiber layer (RNFL) thickness. Design: Retrospective cohort study. Participants: A total of 14,034 SDOCT scans from 816 eyes from 462 individuals. Methods: A DL convolutional neural network was trained to assess SDOCT RNFL thickness measurements of two visits (a baseline and a follow-up) along with time between visits to predict the probability of glaucoma progression. The ground truth was defined by consensus from subjective grading by glaucoma specialists. Diagnostic performance was summarized by the ...

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