Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA.
Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA.
Exp Biol Med (Maywood). 2021 Oct;246(20):2170-2183. doi: 10.1177/15353702211026581. Epub 2021 Jul 19.
Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.
光学相干断层扫描血管造影术 (OCTA) 为在毛细血管水平分辨率下对视网膜血管成像提供了一种非侵入性的无标记解决方案。从原理上讲，提高分辨率意味着有更好的机会揭示与早期无症状的眼部疾病相关的细微微血管扭曲。然而，大规模筛查需要有经验的临床医生手动检查视网膜图像，这可能导致人为错误并阻碍客观筛查。最近，已经开发出定量 OCTA 特征来标准化和记录视网膜血管变化。已经证明了使用定量 OCTA 特征进行不同视网膜病变的机器学习分类的可行性。基于深度学习的应用也被探索用于自动 OCTA 图像分析和疾病分类。本文总结了定量 OCTA 特征、机器学习图像分析和分类的最新进展。
