Artificial intelligence in predicting anti-VEGF treatment response in diabetic macular edema: current progress and future directions

Abstract

Diabetic macular edema (DME), a leading cause of vision impairment in diabetes, is primarily treated with intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections. However, variable treatment response rates often lead to persistent edema and irreversible vision loss. Accurate prediction of treatment response is therefore critical for optimizing treatment strategies and preserving visual function. This review examines the application of artificial intelligence (AI) to predict anti-VEGF treatment outcomes in DME. While optical coherence tomography (OCT) imaging has shown significant progress, the potential of optical coherence tomography angiography (OCTA) and fluorescein angiography (FA) remains under-exploited. AI holds considerable promise for enhancing predictive accuracy. Future research should focus on multimodal imaging approaches integrating structural, ischemic, and vascular information to develop more accurate and reliable predictive models for personalized DME treatment.