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人工智能与光学相干断层成像术

Artificial Intelligence and Optical Coherence Tomography Imaging.

机构信息

From the Harkness Eye Institute, Columbia University, New York, United States.

出版信息

Asia Pac J Ophthalmol (Phila). 2019 Mar-Apr;8(2):187-194. doi: 10.22608/APO.201904. Epub 2019 Apr 18.

Abstract

This review article aimed to highlight the application and use of artificial intelligence (AI) in optical coherence tomography (OCT) imaging in ophthalmology. Artificial intelligence programs seek to simulate intelligent human behavior in computers. With an abundance of patient data, especially with the advent and growing use of imaging modalities such as OCT, AI programs provide us with the unique opportunity to analyze this plethora of information and assist in making clinical decisions in the field of ophthalmology. Groups around the world have developed and evaluated AI programs that gather data from diagnostic modalities, such as OCT, that assist in the diagnosis and management of ophthalmological diseases with a high accuracy. Artificial intelligence programs using OCT have the potential to play a significant role in the diagnosis and management of ophthalmological disease in the near future. Incorporation of AI in medicine, however, is not without its pitfalls. Some limitations of AI in ophthalmology are also discussed in this review. These include the deskilling of physicians due to increase in reliance on automation, inability of AI programs to take a holistic approach to clinical encounters with patients, requirement of pre-existing strong datasets to train AI programs, and the inability of AI programs to incorporate the ambiguity and variability that is intrinsic to the nature of clinical medicine.

摘要

本文旨在强调人工智能(AI)在眼科光学相干断层扫描(OCT)成像中的应用和使用。人工智能程序试图在计算机中模拟智能人类行为。有了大量的患者数据,特别是随着成像模式如 OCT 的出现和广泛使用,人工智能程序为我们提供了独特的机会来分析这些大量的信息,并协助在眼科领域做出临床决策。世界各地的许多团体已经开发和评估了人工智能程序,这些程序从 OCT 等诊断模式中收集数据,以帮助准确诊断和管理眼科疾病。人工智能程序使用 OCT 有潜力在不久的将来在眼科疾病的诊断和管理中发挥重要作用。然而,人工智能在医学中的应用并非没有缺陷。本文还讨论了人工智能在眼科领域的一些局限性。这些局限性包括由于过度依赖自动化而导致医生技能下降、人工智能程序无法全面考虑与患者的临床接触、需要预先存在的强大数据集来训练人工智能程序,以及人工智能程序无法将临床医学固有的模糊性和可变性纳入其中。

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