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糖尿病视网膜病变中超宽视野血管造影血管特征的自动严重程度分类表征

Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification.

作者信息

Sevgi Duriye Damla, Srivastava Sunil K, Whitney Jon, O'Connell Margaret, Kar Sudeshna Sil, Hu Ming, Reese Jamie, Madabhushi Anant, Ehlers Justis P

机构信息

The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.

出版信息

Ophthalmol Sci. 2021 Sep;1(3). doi: 10.1016/j.xops.2021.100049. Epub 2021 Jul 30.

Abstract

PURPOSE

To determine the association between diabetic retinopathy (DR) severity and quantitative retinal vascular features.

DESIGN

Retrospective image analysis study.

PARTICIPANTS

Eyes with DR and eyes with no posterior segment disease (normal eyes) that had undergone ultra-widefield fluorescein angiography (UWFA) with associated color fundus photography. Exclusion criteria were any previous laser photocoagulation, low image quality, intravitreal or periocular pharmacotherapy within 6 months of imaging, and any other significant retinal disease including posterior uveitis, retinal vein occlusion, and choroidal neovascularization.

METHODS

The centered early mid-phase UWFA frame that captured the maximum vessel area was selected using automated custom software for each eye. Panretinal and zonal vascular features were extracted using a machine learning algorithm. Eyes with DR were graded for DR severity as mild nonproliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). Parameters of normal eyes were compared with age- and gender-matched patients with DR using the test. Differences between severity groups were evaluated by the analysis of variance and Kruskal-Wallis tests, generalized linear mixed-effects models, and random forest regression models.

MAIN OUTCOME MEASURES

Diabetic retinopathy severity and vascular features (panretinal and zonal vessel area, length and geodesic distance, panretinal area index, tortuosity measures, vascular density measures, and zero vessel density rate).

RESULTS

Ninety-seven eyes from 60 patients with DR and 12 normal eyes from 12 patients that underwent UWFA for evaluation of fellow eye pathology had images of sufficient quality to be included in this analysis. The mean age was 60 ± 10 years in DR eyes and 46 ± 17 years in normal eyes. Panretinal vessel area, mean geodesic distance, skewness, and kurtosis of local vessel density was significantly higher in normal eyes compared with the age- and gender-matched eyes with DR ( < 0.05). Zero vessel density rate, skewness of vessel density, and mean mid-peripheral geodesic distance were among the most important features for distinguishing mild NPDR from advanced forms of DR and PDR versus eyes without PDR.

CONCLUSIONS

Automated analysis of retinal vasculature demonstrated associations with DR severity and visual and subvisual vascular biomarkers. Further studies are needed to evaluate the clinical significance of these parameters for DR prognosis and therapeutic response.

摘要

目的

确定糖尿病视网膜病变(DR)严重程度与定量视网膜血管特征之间的关联。

设计

回顾性图像分析研究。

参与者

接受过超广角荧光素血管造影(UWFA)及相关彩色眼底照相的DR患眼和无眼后段疾病的眼(正常眼)。排除标准为既往有激光光凝治疗史、图像质量低、成像后6个月内有玻璃体内或眼周药物治疗,以及任何其他严重视网膜疾病,包括后葡萄膜炎、视网膜静脉阻塞和脉络膜新生血管形成。

方法

使用自动化定制软件为每只眼睛选择捕捉最大血管区域的中心早期中期UWFA图像帧。使用机器学习算法提取全视网膜和区域血管特征。将DR患眼按DR严重程度分为轻度非增殖性DR(NPDR)、中度NPDR、重度NPDR和增殖性DR(PDR)。使用t检验将正常眼的参数与年龄和性别匹配的DR患者进行比较。通过方差分析、Kruskal-Wallis检验、广义线性混合效应模型和随机森林回归模型评估严重程度组之间的差异。

主要观察指标

糖尿病视网膜病变严重程度和血管特征(全视网膜和区域血管面积、长度和测地距离、全视网膜面积指数、迂曲度测量、血管密度测量和零血管密度率)。

结果

60例DR患者的97只患眼和12例因评估对侧眼病变而接受UWFA检查的12例患者的12只正常眼中,有图像质量足够高的可纳入本分析。DR患眼的平均年龄为60±10岁,正常眼为46±17岁。与年龄和性别匹配的DR患眼相比,正常眼的全视网膜血管面积、平均测地距离、局部血管密度的偏度和峰度显著更高(P<0.05)。零血管密度率、血管密度偏度和平均中周测地距离是区分轻度NPDR与DR和PDR的晚期形式与无PDR眼的最重要特征之一。

结论

视网膜血管的自动分析显示与DR严重程度以及视觉和亚视觉血管生物标志物有关联。需要进一步研究来评估这些参数对DR预后和治疗反应的临床意义。

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