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

摘要

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.