Department of Ophthalmology, Retina Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
Department of Ophthalmology, Harvard Retinal Imaging Lab, Massachusetts Eye and Ear, Boston, MA, USA.
Semin Ophthalmol. 2021 May 19;36(4):341-345. doi: 10.1080/08820538.2021.1901123. Epub 2021 Mar 18.
Ophthalmology has been at the forefront of medical specialties adopting artificial intelligence. This is primarily due to the "image-centric" nature of the field. Thanks to the abundance of patients' OCT scans, analysis of OCT imaging has greatly benefited from artificial intelligence to expand patient screening and facilitate clinical decision-making.In this review, we define the concepts of artificial intelligence, machine learning, and deep learning and how different artificial intelligence algorithms have been applied in OCT image analysis for disease screening, diagnosis, management, and prognosis.Finally, we address some of the challenges and limitations that might affect the incorporation of artificial intelligence in ophthalmology. These limitations mainly revolve around the quality and accuracy of datasets used in the algorithms and their generalizability, false negatives, and the cultural challenges around the adoption of the technology.
眼科一直处于采用人工智能的医学专业的前沿。这主要是由于该领域的“以图像为中心”的性质。由于大量的患者 OCT 扫描，人工智能在 OCT 成像分析中得到了极大的受益，以扩大患者筛查范围并促进临床决策。在这篇综述中，我们定义了人工智能、机器学习和深度学习的概念，以及不同的人工智能算法如何在 OCT 图像分析中应用于疾病筛查、诊断、管理和预后。最后，我们讨论了一些可能影响人工智能在眼科应用的挑战和局限性。这些局限性主要涉及算法中使用的数据集的质量和准确性及其通用性、假阴性以及围绕采用该技术的文化挑战。
