1. Using Deep Learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images

    Using Deep Learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images

    Purpose To construct and evaluate a Deep Learning (DL) model to diagnose early glaucoma from spectral domain optical coherence tomography (SD-OCT) images. Design AI diagnostic tool development, evaluation, and comparison Methods Setting: multiple institutional practices. Study population Pre-training data consisted of 4316 OCT images (RS3000, Nidek) from 1565 eyes with open angle glaucoma (OAG) irrespective of the stage of glaucoma and 193 normal eyes. Training data included OCT-1000/2000 (Topcon) from 94 eyes of 94 early OAG patients (mean deviation: MD >-5.0 dB) and 84 eyes of 84 normal subjects. Testing data included OCT-1000/2000 from 114 eyes of ...

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