Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME or RVO patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a vascular network organization via Hough transform (VaNgOGH) descriptor generated based on FA images of tissue demonstrating DME or RVO. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME or RVO patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a VaNgOGH descriptor generated based on FA imagery of the patient.
Hough transform-based vascular network disorder features on baseline fluorescein angiography scans predict response to anti-VEGF therapy in diabetic macular edema