The George Washington University Medical Faculty Associates, Washington, DC.
Orbis International, New York, NY.
J Glaucoma. 2022 Mar 1;31(3):137-146. doi: 10.1097/IJG.0000000000001972.
Glaucomatous optic neuropathy is the leading cause of irreversible blindness worldwide. Diagnosis and monitoring of disease involves integrating information from the clinical examination with subjective data from visual field testing and objective biometric data that includes pachymetry, corneal hysteresis, and optic nerve and retinal imaging. This intricate process is further complicated by the lack of clear definitions for the presence and progression of glaucomatous optic neuropathy, which makes it vulnerable to clinician interpretation error. Artificial intelligence (AI) and AI-enabled workflows have been proposed as a plausible solution. Applications derived from this field of computer science can improve the quality and robustness of insights obtained from clinical data that can enhance the clinician's approach to patient care. This review clarifies key terms and concepts used in AI literature, discusses the current advances of AI in glaucoma, elucidates the clinical advantages and challenges to implementing this technology, and highlights potential future applications.
青光眼性视神经病变是全球范围内导致不可逆性失明的主要原因。疾病的诊断和监测需要整合临床检查信息以及来自视野测试的主观数据和客观生物计量学数据，包括角膜厚度、角膜滞后和视神经及视网膜成像。由于缺乏对青光眼性视神经病变存在和进展的明确定义，这使得该过程更加复杂，容易导致临床医生的解释错误。人工智能（AI）和 AI 支持的工作流程被提出作为一种可行的解决方案。源自计算机科学领域的应用程序可以提高从临床数据中获得的见解的质量和稳健性，从而增强临床医生对患者护理的方法。本综述澄清了 AI 文献中使用的关键术语和概念，讨论了 AI 在青光眼领域的最新进展，阐明了实施该技术的临床优势和挑战，并突出了潜在的未来应用。
