Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study.

IF 4.1 Q2 ENDOCRINOLOGY & METABOLISM
John J Wroblewski, Ermilo Sanchez-Buenfil, Miguel Inciarte, Jay Berdia, Lewis Blake, Simon Wroblewski, Alexandria Patti, Gretchen Suter, George E Sanborn
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引用次数: 0

Abstract

Background: To compare the performance of Medios (offline) and EyeArt (online) artificial intelligence (AI) algorithms for detecting diabetic retinopathy (DR) on images captured using fundus-on-smartphone photography in a remote outreach field setting.

Methods: In June, 2019 in the Yucatan Peninsula, 248 patients, many of whom had chronic visual impairment, were screened for DR using two portable Remidio fundus-on-phone cameras, and 2130 images obtained were analyzed, retrospectively, by Medios and EyeArt. Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard.

Results: A total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%).

Conclusions: Medios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.

在尤卡坦半岛使用基于智能手机的眼底摄影和深度学习人工智能筛查糖尿病视网膜病变:实地研究。
背景:目的:比较Medios(离线)和EyeArt(在线)人工智能(AI)算法在远程外联实地环境中使用智能手机眼底摄影拍摄的图像上检测糖尿病视网膜病变(DR)的性能:2019 年 6 月,在尤卡坦半岛,使用两台便携式 Remidio 眼底手机照相机对 248 名患者(其中许多人患有慢性视力障碍)进行了糖尿病视网膜病变筛查,Medios 和 EyeArt 对获得的 2130 幅图像进行了回顾性分析。筛查性能指标也是以临床检查结果为参考标准,采用遮盖式图像分析法进行回顾性测定的:结果:共有 129 名患者被确定患有某种程度的 DR,119 名患者没有 DR。Medios 能够对每位患者进行评估,灵敏度(95% 置信区间 [CIs])为 94%(88%-97%),特异度为 94%(88%-98%)。主要由于摄影师的误差,EyeArt 评估了 156 名患者,灵敏度为 94%(86%-98%),特异度为 86%(77%-93%)。在对 110 名患者进行的正面比较中,Medios 和 EyeArt 的灵敏度分别为 99%(93%-100%)和 95%(87%-99%)。两者的特异性均为 88%(73%-97%):结论:Medios 和 EyeArt 人工智能算法在实际野外环境中检测 DR 时表现出较高的灵敏度和特异性。在需要立即得到结果的远程、大规模 DR 筛查活动中,应考虑使用这两种程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Diabetes Science and Technology
Journal of Diabetes Science and Technology Medicine-Internal Medicine
CiteScore
7.50
自引率
12.00%
发文量
148
期刊介绍: The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.
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