Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, Missouri.
Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland.
Ophthalmol Retina. 2021 Jan;5(1):71-77. doi: 10.1016/j.oret.2020.06.016. Epub 2020 Jun 17.
Retinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that artificial intelligence-assisted nonmydriatic point-of-care screening administered during primary care visits would increase the adherence to recommendations for follow-up eye care in patients with diabetes.
Prospective cohort study.
Adults 18 years of age or older with a clinical diagnosis of diabetes being cared for in a metropolitan primary care practice for low-income patients.
All participants underwent nonmydriatic fundus photography followed by automated retinal image analysis with human supervision. Patients with positive or inconclusive screening results were referred for comprehensive ophthalmic evaluation. Adherence to referral recommendations was recorded and compared with the historical adherence rate from the same clinic.
Rate of adherence to eye screening recommendations.
By automated screening, 8.3% of the 180 study participants had referable diabetic eye disease, 13.3% had vision-threatening disease, and 29.4% showed inconclusive results. The remaining 48.9% showed negative screening results, confirmed by human overread, and were not referred for follow-up ophthalmic evaluation. Overall, the automated platform showed a sensitivity of 100% (confidence interval, 92.3%-100%) in detecting an abnormal screening results, whereas its specificity was 65.7% (confidence interval, 57.0%-73.7%). Among patients referred for follow-up ophthalmic evaluation, the adherence rate was 55.4% at 1 year compared with the historical adherence rate of 18.7% (P < 0.0001, Fisher exact test).
Implementation of an automated diabetic retinopathy screening system in a primary care clinic serving a low-income metropolitan patient population improved adherence to follow-up eye care recommendations while reducing referrals for patients with low-risk features.
视网膜筛查检查可以预防糖尿病导致的视力丧失，但费用高昂且未得到充分利用。我们假设，在初级保健就诊期间进行人工智能辅助的免散瞳即时护理点筛查，将增加糖尿病患者遵循后续眼科护理建议的依从性。
前瞻性队列研究。
在为低收入患者服务的大都市初级保健实践中，年龄在 18 岁或以上且临床诊断为糖尿病的成年人。
所有参与者均接受免散瞳眼底照相检查，然后进行自动化视网膜图像分析，并由人工进行监督。对筛查结果阳性或不确定的患者进行全面眼科评估。记录对转诊建议的依从性，并与同一诊所的历史依从率进行比较。
对眼部筛查建议的依从率。
通过自动化筛查，180 名研究参与者中有 8.3%患有可转诊的糖尿病眼病，13.3%患有威胁视力的疾病，29.4%的结果不确定。其余 48.9%的筛查结果为阴性，经人工复查确认，未转诊进行后续眼科评估。总的来说，自动化平台在检测异常筛查结果方面的敏感性为 100%（置信区间，92.3%-100%），特异性为 65.7%（置信区间，57.0%-73.7%）。在转诊进行后续眼科评估的患者中，1 年时的依从率为 55.4%，而历史依从率为 18.7%（P<0.0001，Fisher 确切检验）。
在为低收入大都市患者人群服务的初级保健诊所中实施自动化糖尿病视网膜病变筛查系统，在减少低风险特征患者转诊的同时，提高了对后续眼科护理建议的依从性。
