Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disease assessment and monitoring. Artificial intelligence (AI)- assisted tools to detect and assess PACD have proliferated in recent years with encouraging results. Machine learning (ML) algorithms that utilize clinical data have been developed to categorize angle closure eyes by disease mechanism. Other ML algorithms that utilize image data have demonstrated good performance in detecting angle closure. Nonetheless, deep learning (DL) algorithms trained directly on image data generally outperformed traditional ML algorithms in detecting PACD, were able to accurately differentiate between angle status (open, narrow, closed), and automated the measurement of quantitative parameters. However, more work is required to expand the capabilities of these AI algorithms and for deployment into real-world practice settings. This includes the need for real-world evaluation, establishing the use case for different algorithms, and evaluating the feasibility of deployment while considering other clinical, economic, social, and policy-related factors.

原发性闭角型青光眼是一种视力衰弱的疾病，在全世界范围内未被发现。治疗原发性闭角疾病 (PACD) 的许多挑战与缺乏方便、精确的临床疾病评估和监测工具有关。近年来，用于检测和评估 PACD 的人工智能 (AI) 辅助工具激增，并取得了令人鼓舞的结果。利用临床数据的机器学习 (ML) 算法已被开发出来，可以根据疾病机制对闭角眼进行分类。其他利用图像数据的机器学习算法在检测角度闭合方面表现出了良好的性能。尽管如此，直接在图像数据上训练的深度学习 (DL) 算法在检测 PACD 方面通常优于传统的 ML 算法，能够准确地区分角度状态（开放、狭窄、闭合），并自动测量定量参数。然而，还需要做更多的工作来扩展这些人工智能算法的功能并将其部署到现实世界的实践环境中。这包括需要进行现实世界的评估，建立不同算法的用例，以及在考虑其他临床、经济、社会和政策相关因素的同时评估部署的可行性。