Yousefi Siamak
Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA.
Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
J Ophthalmic Vis Res. 2023 Feb 21;18(1):97-112. doi: 10.18502/jovr.v18i1.12730. eCollection 2023 Jan-Mar.
Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AI-enabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice.
眼科是医学中主要的影像密集型领域之一,因此人工智能(AI)在推进眼部疾病的诊断、药物疗效及其他治疗相关方面具有广泛应用的潜力。在过去几年里,AI在眼科领域取得了令人瞩目的进展,有两个具备自主功能的AI系统已获得美国监管部门批准,可自主筛查中度或重度糖尿病视网膜病变及黄斑水肿。虽然尚无用于青光眼筛查的具备自主功能的AI系统获得美国监管部门批准,但已有许多具备辅助功能的AI软件工具应用于商业化仪器中,用于量化视网膜图像和视野,以促进青光眼研究和临床实践。在这篇文献综述(非系统性)中,我们概述了AI在青光眼方面的应用,并强调了AI整合及应用于临床实践的一些局限性和注意事项。