Artificial intelligence in OCT angiography

被引:87
作者
Hormel, Tristan T. [1 ]
Hwang, Thomas S. [1 ]
Bailey, Steven T. [1 ]
Wilson, David J. [1 ]
Huang, David [1 ]
Jia, Yali [1 ,2 ]
机构
[1] Oregon Hlth & Sci Univ, Casey Eye Inst, Portland, OR 97239 USA
[2] Oregon Hlth & Sci Univ, Dept Biomed Engn, Portland, OR 97239 USA
基金
美国国家卫生研究院;
关键词
OCT Angiography; Artificial intelligence; Deep learning; Image analysis; OPTICAL COHERENCE TOMOGRAPHY; RETINAL LAYER SEGMENTATION; AMPLITUDE-DECORRELATION ANGIOGRAPHY; CONVOLUTIONAL NEURAL-NETWORK; ARTERY-VEIN DIFFERENTIATION; AUTOMATIC SEGMENTATION; DIABETIC-RETINOPATHY; MOTION CORRECTION; VESSEL DENSITY; CHOROIDAL NEOVASCULARIZATION;
D O I
10.1016/j.preteyeres.2021.100965
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that provides threedimensional, information-rich vascular images. With numerous studies demonstrating unique capabilities in biomarker quantification, diagnosis, and monitoring, OCTA technology has seen rapid adoption in research and clinical settings. The value of OCTA imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features and pathology. Today, the most powerful image analysis methods are based on artificial intelligence (AI). While AI encompasses a large variety of techniques, machinelearning-based, and especially deep-learning-based, image analysis provides accurate measurements in a variety of contexts, including different diseases and regions of the eye. Here, we discuss the principles of both OCTA and AI that make their combination capable of answering new questions. We also review contemporary applications of AI in OCTA, which include accurate detection of pathologies such as choroidal neovascularization, precise quantification of retinal perfusion, and reliable disease diagnosis.
引用
收藏
页数:26
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