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Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies

机译:使用智能手机检测糖尿病视网膜病:诊断测试精度研究的审查评论和荟萃分析

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Background Diabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphone ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible, and easier to use than conventional methods. Objective This study aimed to determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients. Methods We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients, which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR to an accurate or commonly employed reference standard, such as indirect ophthalmoscopy, slit-lamp biomicroscopy, and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data, and assessed the quality of included studies using the Quality Assessment of Diagnostic Accuracy Studies–2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines. Results In all, nine studies involving 1430 participants were included. Most studies were of high quality, except one study with limited applicability because of its reference standard. The pooled sensitivity and specificity for detecting any DR was 87% (95% CI 74%-94%) and 94% (95% CI 81%-98%); mild nonproliferative DR (NPDR) was 39% (95% CI 10%-79%) and 95% (95% CI 91%-98%); moderate NPDR was 71% (95% CI 57%-81%) and 95% (95% CI 88%-98%); severe NPDR was 80% (95% CI 49%-94%) and 97% (95% CI 88%-99%); proliferative DR (PDR) was 92% (95% CI 79%-97%) and 99% (95% CI 96%-99%); diabetic macular edema was 79% (95% CI 63%-89%) and 93% (95% CI 82%-97%); and referral-warranted DR was 91% (95% CI 86%-94%) and 89% (95% CI 56%-98%). The area under SROC curve ranged from 0.879 to 0.979. The diagnostic odds ratio ranged from 11.3 to 1225. Conclusions We found heterogeneous evidence showing that smartphone ophthalmoscopy performs well in detecting DR. The diagnostic accuracy for PDR was highest. Future studies should standardize reference criteria and classification criteria and evaluate other available forms of smartphone ophthalmoscopy in primary care settings.
机译:背景技术糖尿病视网膜病变(DR),糖尿病常见并发症,是全世界成人视力受损的主要原因。智能手机眼科检查涉及使用智能手机相机进行数字视网膜成像。利用智能手机检测DR可能比传统方法更具价格更实惠,可访问,更容易使用。目的本研究旨在确定各种智能手机眼镜检查方法检测糖尿病患者博士的诊断准确性。方法我们在2018年1月至2018年1月出版的文献中的医学文献分析和检索系统(Medline),Embase和Cochrane图书馆的电子搜索。我们包括涉及糖尿病患者的研究,这比较了智能手机眼镜检查的诊断准确性检测DR以准确或常用的参考标准,例如间接眼镜检查,狭缝灯生物显微镜和桌面眼底摄影。两位审阅者独立筛查了纳入标准,提取数据的研究,并评估了使用诊断准确性研究-2工具的质量评估的包括研究的质量,分歧通过达成共识解决。使用随机效应模型汇集了灵敏度和特异性。构建了概要接收器操作特征(SROC)曲线。本次审查符合首选报告项目,用于系统审查和诊断测试准确性研究指南的诊断测试准则和荟萃分析。结果所有涉及1430名参与者的九项研究。大多数研究具有高质量,除了一个具有有限的适用性的研究,因为其参考标准。汇集的敏感性和检测任何DR的特异性为87%(95%CI 74%-94%)和94%(95%CI 81%-98%);温和的非促使DR(NPDR)为39%(95%CI 10%-79%)和95%(95%CI 91%-98%);中等NPDR为71%(95%CI 57%-81%)和95%(95%CI 88%-98%);严重的NPDR为80%(95%CI 49%-94%)和97%(95%CI 88%-99%);增殖性DR(PDR)为92%(95%CI 79%-97%)和99%(95%CI 96%-99%);糖尿病黄斑水肿为79%(95%CI 63%-89%)和93%(95%CI 82%-97%);和转介保证的DR为91%(95%CI 86%-94%)和89%(95%CI 56%-98%)。 SROC曲线下的区域范围为0.879至0.979。诊断赔率比率范围为11.3至1225.结论我们发现异构证据表明智能手机眼镜镜检查在检测博士方面都很顺利。 PDR的诊断准确性最高。未来的研究应标准化参考标准和分类标准,并在初级保健环境中评估其他可用形式的智能手机眼镜检查。

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