Sobhi Navid, Sadeghi-Bazargani Yasin, Mirzaei Majid, Abdollahi Mirsaeed, Jafarizadeh Ali, Pedrammehr Siamak, Alizadehsani Roohallah, Tan Ru-San, Islam Sheikh Mohammed Shariful, Acharya U Rajendra
Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
J Diabetes Metab Disord. 2025 Apr 12;24(1):104. doi: 10.1007/s40200-025-01596-7. eCollection 2025 Jun.
Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vasculature imaging serves as a valuable indicator of both microvascular and macrovascular health. Moreover, artificial intelligence (AI)-enabled systems developed for high-throughput detection of diabetic retinopathy (DR) using digitized retinal images have become clinically adopted. This study reviews AI applications using retinal images for DM-related complications, highlighting advancements beyond DR screening, diagnosis, and prognosis, and addresses implementation challenges, such as ethics, data privacy, equitable access, and explainability.
We conducted a thorough literature search across several databases, including PubMed, Scopus, and Web of Science, focusing on studies involving diabetes, the retina, and artificial intelligence. We reviewed the original research based on their methodology, AI algorithms, data processing techniques, and validation procedures to ensure a detailed analysis of AI applications in diabetic retinal imaging.
Retinal images can be used to diagnose DM complications including DR, neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as to predict the risk of cardiovascular events. Beyond DR screening, AI integration also offers significant potential to address the challenges in the comprehensive care of patients with DM.
With the ability to evaluate the patient's health status in relation to DM complications as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.
糖尿病（DM）会增加血管并发症的风险，视网膜血管成像可作为微血管和大血管健康状况的重要指标。此外，利用数字化视网膜图像开发的用于高通量检测糖尿病视网膜病变（DR）的人工智能（AI）系统已在临床上得到应用。本研究回顾了利用视网膜图像对糖尿病相关并发症进行的人工智能应用，重点介绍了除糖尿病视网膜病变筛查、诊断和预后之外的进展，并探讨了实施过程中面临的挑战，如伦理、数据隐私、公平获取和可解释性等问题。
我们在多个数据库（包括PubMed、Scopus和Web of Science）中进行了全面的文献检索，重点关注涉及糖尿病、视网膜和人工智能的研究。我们根据其方法、人工智能算法、数据处理技术和验证程序对原始研究进行了综述，以确保对糖尿病视网膜成像中的人工智能应用进行详细分析。
视网膜图像可用于诊断包括糖尿病视网膜病变、神经病变、肾病和动脉粥样硬化性心血管疾病在内的糖尿病并发症，还可用于预测心血管事件的风险。除糖尿病视网膜病变筛查外，人工智能的整合还为应对糖尿病患者综合护理中的挑战提供了巨大潜力。
人工智能辅助的视网膜图像分析能够评估患者与糖尿病并发症相关的健康状况以及未来心血管并发症的风险预后，有可能成为现代糖尿病患者个性化医疗的核心工具。
