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通过自动视网膜图像分析系统检测出的糖尿病性黄斑水肿误诊率及预测因素

Rate and Predictors of Misclassification of Active Diabetic Macular Edema as Detected by an Automated Retinal Image Analysis System.

作者信息

La Franca Lamberto, Rutigliani Carola, Checchin Lisa, Lattanzio Rosangela, Bandello Francesco, Cicinelli Maria Vittoria

机构信息

Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, IRCCS Ospedale San Raffaele, University Vita-Salute, Via Olgettina 60, 20132, Milan, Italy.

School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.

出版信息

Ophthalmol Ther. 2024 Jun;13(6):1553-1567. doi: 10.1007/s40123-024-00929-8. Epub 2024 Apr 8.


DOI:10.1007/s40123-024-00929-8
PMID:38587776
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11109071/
Abstract

INTRODUCTION: The aim of this work is to estimate the sensitivity, specificity, and misclassification rate of an automated retinal image analysis system (ARIAS) in diagnosing active diabetic macular edema (DME) and to identify factors associated with true and false positives. METHODS: We conducted a cross-sectional study of prospectively enrolled patients with diabetes mellitus (DM) referred to a tertiary medical retina center for screening or management of DME. All patients underwent two-field fundus photography (macula- and disc-centered) with a true-color confocal camera; images were processed by EyeArt V.2.1.0 (Woodland Hills, CA, USA). Active DME was defined as the presence of intraretinal or subretinal fluid on spectral-domain optical coherence tomography (SD-OCT). Sensitivity and specificity and their 95% confidence intervals (CIs) were calculated. Variables associated with true (i.e., DME labeled as present by ARIAS + fluid on SD-OCT) and false positives (i.e., DME labeled as present by ARIAS + no fluid on SD-OCT) of active DME were explored. RESULTS: A total of 298 eyes were included; 92 eyes (31%) had active DME. ARIAS sensitivity and specificity were 82.61% (95% CI 72.37-89.60) and 84.47% (95% CI 78.34-89.10). The misclassification rate was 16%. Factors associated with true positives included younger age (p = 0.01), shorter DM duration (p = 0.006), presence of hard exudates (p = 0.005), and microaneurysms (p = 0.002). Factors associated with false positives included longer DM duration (p = 0.01), worse diabetic retinopathy severity (p = 0.008), history of inactivated DME (p < 0.001), and presence of hard exudates (p < 0.001), microaneurysms (p < 0.001), or epiretinal membrane (p = 0.06). CONCLUSIONS: The sensitivity of ARIAS was diminished in older patients and those without DME-related fundus lesions, while the specificity was reduced in cases with a history of inactivated DME. ARIAS performed well in screening for naïve DME but is not effective in surveillance inactivated DME.

摘要

引言:本研究旨在评估自动视网膜图像分析系统(ARIAS)诊断活动性糖尿病性黄斑水肿(DME)的敏感性、特异性和错误分类率,并确定与真阳性和假阳性相关的因素。 方法:我们对前瞻性纳入的糖尿病(DM)患者进行了一项横断面研究,这些患者被转诊至三级医疗视网膜中心进行DME筛查或治疗。所有患者均使用真彩色共聚焦相机进行双视野眼底摄影(以黄斑和视盘为中心);图像由EyeArt V.2.1.0(美国加利福尼亚州伍德兰希尔斯)处理。活动性DME定义为光谱域光学相干断层扫描(SD-OCT)上存在视网膜内或视网膜下液。计算敏感性、特异性及其95%置信区间(CI)。探讨与活动性DME的真阳性(即ARIAS标记为存在+SD-OCT上有液)和假阳性(即ARIAS标记为存在+SD-OCT上无液)相关的变量。 结果:共纳入298只眼;92只眼(31%)有活动性DME。ARIAS的敏感性和特异性分别为82.61%(95%CI 72.37-89.60)和84.47%(95%CI 78.34-89.10)。错误分类率为16%。与真阳性相关的因素包括年龄较小(p=0.01)、DM病程较短(p=0.006)、存在硬性渗出(p=0.005)和微动脉瘤(p=0.002)。与假阳性相关的因素包括DM病程较长(p=0.01)、糖尿病视网膜病变严重程度较差(p=0.008)、既往有非活动性DME病史(p<0.001)以及存在硬性渗出(p<0.001)、微动脉瘤(p<0.001)或视网膜前膜(p=0.06)。 结论:ARIAS在老年患者和无DME相关眼底病变的患者中敏感性降低,而在有非活动性DME病史的患者中特异性降低。ARIAS在筛查初发DME方面表现良好,但在监测非活动性DME方面无效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e4c/11109071/26713c657d2f/40123_2024_929_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e4c/11109071/26713c657d2f/40123_2024_929_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e4c/11109071/26713c657d2f/40123_2024_929_Fig1_HTML.jpg

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Rate and Predictors of Misclassification of Active Diabetic Macular Edema as Detected by an Automated Retinal Image Analysis System.

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引用本文的文献

[1]
Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Software: IDx-DR and RetCAD.

Ophthalmol Ther. 2025-1

[2]
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本文引用的文献

[1]
Characterization of the Structural and Functional Alteration in Eyes with Diabetic Macular Ischemia.

Ophthalmol Retina. 2023-2

[2]
Prevalence of diabetic macular edema based on optical coherence tomography in people with diabetes: A systematic review and meta-analysis.

Surv Ophthalmol. 2022

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Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy.

JAMA Netw Open. 2021-11-1

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Dis Mon. 2021-5

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Evaluation of a New Model of Care for People with Complications of Diabetic Retinopathy: The EMERALD Study.

Ophthalmology. 2021-4

[6]
Calculating Sensitivity, Specificity, and Predictive Values for Correlated Eye Data.

Invest Ophthalmol Vis Sci. 2020-9-1

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Prog Retin Eye Res. 2021-5

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Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients.

Br J Ophthalmol. 2021-5

[9]
Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care.

Ophthalmol Retina. 2021-1

[10]
Assessing Intereye Symmetry and Its Implications for Study Design.

Invest Ophthalmol Vis Sci. 2020-6-3

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