Artificial intelligence in proliferative diabetic retinopathy: advancing diagnosis, precision surgery, and anti-VEGF therapy optimization - Ke, Geng-Qian; Fu, Yang-Jun; Huang, Zhuo-Han; Dai, Shi-Xue; Wen, Yun-Hua; Lv, Hai-Xiang (Frontiers in Medicine, 2025)
Proliferative diabetic retinopathy (PDR) represents the most advanced and vision-threatening stage of diabetic retinopathy (DR) and remains a leading cause of blindness in individuals with diabetes. This review presents a comprehensive overview of recent advances in the application of artificial intelligence (AI) for the diagnosis and treatment of PDR, emphasizing its clinical potential and associated challenges. The role of vascular endothelial growth factor (VEGF) in the pathogenesis of PDR has become increasingly clear, and AI offers novel capabilities in retinal image analysis, disease progression prediction, and treatment decision-making. These advancements have notably improved diagnostic accuracy and efficiency. Furthermore, AI-based models show promise in optimizing anti-VEGF therapy by enhancing therapeutic outcomes while reducing unnecessary healthcare expenditures. Future research should focus on the safe, effective, and ethical integration of AI into clinical workflows. Overcoming practical deployment barriers will require interdisciplinary collaboration among technology developers, clinicians, and regulatory bodies. The strategies and frameworks discussed in this review are expected to provide a foundation for future AI research and clinical translation in fields of PDR.