AlRyalat Saif Aldeen, Singh Praveer, Kalpathy-Cramer Jayashree, Kahook Malik Y
Department of Ophthalmology, The University of Jordan, Amman, 11942, Jordan.
Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USA.
Clin Ophthalmol. 2023 May 31;17:1525-1530. doi: 10.2147/OPTH.S410905. eCollection 2023.
There has been a recent surge in the number of publications centered on the use of artificial intelligence (AI) to diagnose various systemic diseases. The Food and Drug Administration has approved several algorithms for use in clinical practice. In ophthalmology, most advances in AI relate to diabetic retinopathy, which is a disease process with agreed upon diagnostic and classification criteria. However, this is not the case for glaucoma, which is a relatively complex disease without agreed-upon diagnostic criteria. Moreover, currently available public datasets that focus on glaucoma have inconstant label quality, further complicating attempts at training AI algorithms efficiently. In this perspective paper, we discuss specific details related to developing AI models for glaucoma and suggest potential steps to overcome current limitations.
最近，以使用人工智能（AI）诊断各种全身性疾病为中心的出版物数量激增。美国食品药品监督管理局已批准了几种算法用于临床实践。在眼科领域，AI的大多数进展都与糖尿病视网膜病变有关，这是一种具有公认诊断和分类标准的疾病过程。然而，青光眼并非如此，青光眼是一种相对复杂的疾病，没有公认的诊断标准。此外，目前可用的专注于青光眼的公共数据集的标签质量不稳定，这使得有效训练AI算法的尝试更加复杂。在这篇观点论文中，我们讨论了与开发青光眼AI模型相关的具体细节，并提出了克服当前局限性的潜在步骤。
