Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy

糖尿病性视网膜病变 眼科 医学 差速器(机械装置) 内科学 心脏病学 糖尿病 内分泌学 物理 热力学
作者
Mansour Abtahi,David Le,Behrouz Ebrahimi,Albert K. Dadzie,Mojtaba Rahimi,Yi‐Ting Hsieh,Michael J. Heiferman,Jennifer I. Lim,Xincheng Yao
出处
期刊:Investigative Ophthalmology & Visual Science [Cadmus Press]
卷期号:65 (10): 20-20 被引量:2
标识
DOI:10.1167/iovs.65.10.20
摘要

Purpose: This study aimed to investigate the impact of distinctive capillary–large vessel (CLV) analysis in optical coherence tomography angiography (OCTA) on the classification performance of diabetic retinopathy (DR). Methods: This multicenter study analyzed 212 OCTA images from 146 patients, including 28 controls, 36 diabetic patients without DR (NoDR), 31 with mild non-proliferative DR (NPDR), 28 with moderate NPDR, and 23 with severe NPDR. Quantitative features were derived from the whole image as well as the parafovea and perifovea regions. A support vector machine classifier was employed for DR classification. The accuracy and area under the receiver operating characteristic curve were used to evaluate the classification performance, utilizing features derived from the whole image and specific regions, both before and after CLV analysis. Results: Differential CLV analysis significantly improved OCTA classification of DR. In binary classifications, accuracy improved by 11.81%, rising from 77.45% to 89.26%, when utilizing whole image features. For multiclass classifications, accuracy increased by 7.55%, from 78.68% to 86.23%. Incorporating features from the whole image, parafovea, and perifovea further improved binary classification accuracy from 83.07% to 93.80%, and multiclass accuracy from 82.64% to 87.92%. Conclusions: This study demonstrated that feature changes in capillaries are more sensitive during DR progression, and CLV analysis can significantly improve DR classification performance by extracting features that are specific to large vessels and capillaries in OCTA. Incorporating regional features further improves DR classification accuracy. Differential CLV analysis promises better disease screening, diagnosis, and treatment outcome assessment.
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