1. Deep learning image analysis of optical coherence tomography angiography measured vessel density improves classification of healthy and glaucoma eyes

    Deep learning image analysis of optical coherence tomography angiography measured vessel density improves classification of healthy and glaucoma eyes

    Purpose : To compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument provided, feature-based optical coherence tomography angiography (OCTA) vessel density measurements and OCT RNFL thickness measurements for classifying healthy and glaucomatous eyes. Design : Comparison of diagnostic approaches Methods : 130 eyes of 80 healthy individuals and 275 eyes of 185 glaucoma patients with optic nerve head (ONH) OCTA and OCT imaging were included. Classification performance of a VGG16 CNN trained and tested on entire en face 4.5 mm x 4.5 mm radial peripapillary capillary OCTA ONH images was ...

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