Bunod R, Augstburger E, Brasnu E, Labbe A, Baudouin C
Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France.
Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France.
J Fr Ophtalmol. 2022 Feb;45(2):216-232. doi: 10.1016/j.jfo.2021.11.002. Epub 2022 Jan 3.
In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.
近年来，人工智能（AI）研究在眼科领域，尤其是青光眼方面经历了前所未有的蓬勃发展。青光眼的诊断和随访复杂，依赖于一系列临床证据和辅助检查。来自视神经和黄斑结构及功能检测的大量信息，使得青光眼成为AI应用的一个特别合适的领域。在本文中，我们将综述在青光眼领域使用AI的研究工作，无论是用于筛查、诊断还是进展检测。许多AI策略在使用眼底摄影、光学相干断层扫描或自动视野计进行青光眼检测方面已显示出有前景的结果。这些成像方式的结合提高了AI算法的性能，其结果与人类相当。我们将讨论此类模型的潜在应用以及部署和验证中的障碍与局限性。虽然毫无疑问AI有潜力彻底改变青光眼的管理和筛查，但未来几年的研究需要解决关于此类结果的临床意义和预测的可解释性等不可避免的问题。
