REVIEW article
Front. Med.
Sec. Ophthalmology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1644456
This article is part of the Research TopicEfficient Artificial Intelligence in Ophthalmic Imaging – Volume IIView all 11 articles
Artificial Intelligence in Proliferative Diabetic Retinopathy: Advancing Diagnosis, Precision Surgery, and Anti-VEGF Therapy Optimization
Provisionally accepted
- 1Guangdong Provincial People's Hospital, Guangzhou, China
- 2Southern Medical University, Guangzhou, China
- 3Nanchang University Fuzhou Medical College, Fuzhou, China
- 4Department of Ultrasound, Sanyuanli Street Community Health Service Center, Guangzhou, China
- 5Guangdong Provincial People's Hospital Ganzhou Hospital, Ganzhou, China
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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.
Keywords: anti-VEGF therapy, deep learning, machine learning, proliferative diabetic retinopathy, artificial intelligence
Received: 10 Jun 2025; Accepted: 12 Aug 2025.
Copyright: © 2025 DAI, Ke, Fu, Huang, Wen and Lv. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: SHI XUE DAI, Guangdong Provincial People's Hospital, Guangzhou, China
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