Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME 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 set of graph-network features and a set of morphological features generated based on FA images of tissue demonstrating DME. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a set of graph-network features and a set of morphological features generated based on FA imagery of the patient.
Predicting response to anti-vascular endothelial growth factor therapy with computer-extracted morphology and spatial arrangement features of leakage patterns on baseline fluorescein angiography in diabetic macular edema