The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes
The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes
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DOI：
10.1089/dia.2019.0164
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发表时间：
2019-08-07
影响因子：
5.4
通讯作者：
Solanki, Kaushal
中科院分区：
文献类型：
--
作者：
Bhaskaranand, Malavika;Ramachandra, Chaithanya;Solanki, Kaushal
背景：目前由眼科保健专家进行的人工糖尿病视网膜病变（DR）筛查无法扩展到筛查越来越多的有视力丧失风险的糖尿病患者。EyeArt系统是一种自动化的、基于云的人工智能（AI）眼部筛查技术，旨在通过对患者视网膜图像的自动分析，立即轻松检测转诊保证DR。方法：本回顾性研究评估了EyeArt系统v2.0的诊断效果，分析了来自404个初级保健诊所的101,710例连续就诊患者的850,908张眼底图像。EyeArt系统在每次患者就诊时自动检测是否存在转诊保证DR（轻度非增殖性DR [NPDR]以上），并将其性能与经过严格培训的认证眼科医生和验光师的质量保证分级的临床参考标准进行比较。结果：101710次就诊中，75.7%无法转诊，19.3%转诊给眼科专科医生，5.0%的患者根据临床参考标准DR水平未知。EyeArt筛查的灵敏度为91.3%(95%可信区间[CI]: 90.9-91.7)，特异性为91.1% （95% CI: 90.9-91.3）。对于5446例可能可治疗的DR（中度以上的NPDR和/或糖尿病性黄斑水肿），该系统提供了5363例阳性的“参考”输出，灵敏度达到98.5%。结论：本研究捕获了现实世界临床实践中的变化，并表明人工智能DR筛查系统在现实世界中是安全有效的。这项研究表明，对于内分泌学家、糖尿病学家和全科医生来说，这种易于使用的自动化工具的价值，可以满足日益增长的DR筛查和监测需求。
Background: Current manual diabetic retinopathy (DR) screening using eye care experts cannot scale to screen the growing population of diabetes patients who are at risk for vision loss. EyeArt system is an automated, cloud-based artificial intelligence (AI) eye screening technology designed to easily detect referral-warranted DR immediately through automated analysis of patient's retinal images. Methods: This retrospective study assessed the diagnostic efficacy of the EyeArt system v2.0 analyzing 850,908 fundus images from 101,710 consecutive patient visits, collected from 404 primary care clinics. Presence or absence of referral-warranted DR (more than mild nonproliferative DR [NPDR]) was automatically detected by the EyeArt system for each patient encounter, and its performance was compared against a clinical reference standard of quality-assured grading by rigorously trained certified ophthalmologists and optometrists. Results: Of the 101,710 visits, 75.7% were nonreferable, 19.3% were referable to an eye care specialist, and in 5.0%, the DR level was unknown as per the clinical reference standard. EyeArt screening had 91.3% (95% confidence interval [CI]: 90.9-91.7) sensitivity and 91.1% (95% CI: 90.9-91.3) specificity. For 5446 encounters with potentially treatable DR (more than moderate NPDR and/or diabetic macular edema), the system provided a positive "refer" output to 5363 encounters achieving sensitivity of 98.5%. Conclusions: This study captures variations in real-world clinical practice and shows that an AI DR screening system can be safe and effective in the real world. This study demonstrates the value of this easy-to-use, automated tool for endocrinologists, diabetologists, and general practitioners to address the growing need for DR screening and monitoring.