We present updates upon our novel machine-learning methods for the acquisition, processing, and classification of Optical Coherence Tomography Angiography (OCT-A) images. Transitioning from traditional registration methods to machine-learning based methods provided significant reductions in computation time for serial image acquisition and averaging. Through a vessel segmentation network, clinically useful parameters were extracted and then fed to our classification network which was able to classify different diabetic retinopathy severities. The DNN pipeline was also implemented on data acquired with Sensorless Adaptive Optics OCT-A. This work has potential to subsequently reduce clinical overhead and help expedite treatments, resulting in improved patient prognoses.
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