Hybrid 3d-2d Deep Learning For Detection Of Neovascularage-Related Macular Degeneration Using Optical Coherence Tomography B-Scans And Angiography Volumes.

With the availability and increasing reliance on the noninvasive Optical Coherence Tomography Angiography(OCTA) imaging technique for detection of vascular diseases of the retina,suchasage-related macular degeneration(AMD),clinicians now have access to more data than they can effectively parse and digest. Artificial intelligence in the form of convolutional neural networks (CNNs), have shown successful detection of AMDvs. no AMD from fundus images as well as from OCT structural images. In this work, we address an ovel classification problem: automated detection of late stage of the disease, neovascular AMD, visualized through presence of choroidal neovascularization (CNV) and its sequelae. We describe hybrid 3D-2D CNNs that achieve accuracy up to 77.8% at multi-class categorical classification of non-AMD eyes, eyes having non-neovascular AMD, and eyes having neovascular AMD, offering a first-of-its-kind deep learning approach for differentiating progression in AMD.