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 ...