State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong.
Kiang Wu hospital, Macau.
Asia Pac J Ophthalmol (Phila). 2023;12(5):486-494. doi: 10.1097/APO.0000000000000583. Epub 2022 Dec 13.
Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%-50% of DME patients do not respond well to anti-VEGF agents. In addition, there is no evidence-based and universally accepted administration regimen. The identification of DME patients not responding to anti-VEGF agents and the determination of the optimal administration interval are the 2 major challenges of DME, which are difficult to achieve with the coarse granularity of conventional health care modality. Therefore, more and more retina specialists have pointed out the necessity of introducing precision medicine into the management of DME and have conducted related studies in recent years. One of the most frontier methods is the targeted extraction of individualized disease features from optical coherence tomography images based on artificial intelligence technology, which provides precise evaluation and risk classification of DME. This review aims to provide an overview of the progress of artificial intelligence-enabled precision medicine in automated screening, precise evaluation, prognosis prediction, and follow-up monitoring of DME. Further, the challenges ahead of real-world applications and the future development of precision medicine in DME will be discussed.
糖尿病性黄斑水肿（DME）是糖尿病患者中心视力损害的主要原因，也是工作年龄段人群可预防失明的主要原因。随着光相干断层扫描和抗血管内皮生长因子（anti-VEGF）治疗的出现，DME 的诊断、评估和治疗在过去十年中发生了重大变革。然而，DME 患者之间存在巨大的异质性，30%-50%的 DME 患者对 anti-VEGF 药物反应不佳。此外，目前还没有基于证据的、普遍接受的给药方案。确定对 anti-VEGF 药物无反应的 DME 患者，并确定最佳给药间隔，是 DME 的两大挑战，这很难通过常规医疗模式的粗粒度来实现。因此，越来越多的视网膜专家指出，在 DME 的管理中引入精准医学的必要性，并在近年来开展了相关研究。最前沿的方法之一是基于人工智能技术从光相干断层扫描图像中提取个体化疾病特征，为 DME 提供精确的评估和风险分类。本综述旨在概述人工智能支持的精准医学在 DME 的自动化筛查、精确评估、预后预测和随访监测方面的进展。此外，还将讨论实际应用面临的挑战以及 DME 精准医学的未来发展。
