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机器学习在青光眼视盘和视野检测中的应用

Machine Learning in the Detection of the Glaucomatous Disc and Visual Field.

作者信息

Smits David J, Elze Tobias, Wang Haobing, Pasquale Louis R

机构信息

a Department of Ophthalmology , Massachusetts Eye and Ear Infirmary, Harvard Medical School , Boston , USA.

b Schepens Eye Research Institute , Massachusetts Eye and Ear Infirmary, Harvard Medical School , Boston , USA.

出版信息

Semin Ophthalmol. 2019;34(4):232-242. doi: 10.1080/08820538.2019.1620801. Epub 2019 May 27.

Abstract

Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is of utmost importance as there is abundant evidence that early treatment prevents disease progression, preserves vision, and improves patients' long-term quality of life. The structure and function thresholds that alert to the diagnosis of glaucoma can be obtained entirely via digital means, and as such, screening is well suited to benefit from artificial intelligence and specifically machine learning. This paper reviews the concepts and current literature on the use of machine learning for detection of the glaucomatous disc and visual field.

摘要

青光眼是全球不可逆性失明的主要原因。早期检测至关重要,因为有大量证据表明早期治疗可预防疾病进展、保护视力并改善患者的长期生活质量。用于提示青光眼诊断的结构和功能阈值完全可以通过数字手段获得,因此,筛查非常适合借助人工智能,特别是机器学习来获益。本文综述了关于使用机器学习检测青光眼视盘和视野的概念及当前文献。

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