Vought Rita, Vought Victoria, Shah Megh, Szirth Bernard, Bhagat Neelakshi
The Institute of Ophthalmology and Visual Science (IOVS), Rutgers-New Jersey Medical School (Rutgers NJMS), 90 Bergen St., Suite 6100, Newark, NJ, 07103, USA.
Int Ophthalmol. 2023 Dec;43(12):4851-4859. doi: 10.1007/s10792-023-02887-9. Epub 2023 Oct 17.
Early detection and treatment of diabetic retinopathy (DR) are critical for decreasing the risk of vision loss and preventing blindness. Community vision screenings may play an important role, especially in communities at higher risk for diabetes. To address the need for increased DR detection and referrals, we evaluated the use of artificial intelligence (AI) for screening DR.
Patient images of 124 eyes were obtained using a 45° Canon Non-Mydriatic CR-2 Plus AF retinal camera in the Department of Endocrinology Clinic (Newark, NJ) and in a community screening event (Newark, NJ). Images were initially classified by an onsite grader and uploaded for analysis by EyeArt, a cloud-based AI software developed by Eyenuk (California, USA). The images were also graded by an off-site retina specialist. Using Fleiss kappa analysis, a correlation was investigated between the three grading systems, the AI, onsite grader, and a US board-certified retina specialist, for a diagnosis of DR and referral pattern.
The EyeArt results, onsite grader, and the retina specialist had a 79% overall agreement on the diagnosis of DR: 86 eyes with full agreement, 37 eyes with agreement between two graders, 1 eye with full disagreement. The kappa value for concordance on a diagnosis was 0.69 (95% CI 0.61-0.77), indicating substantial agreement. Referral patterns by EyeArt, the onsite grader, and the ophthalmologist had an 85% overall agreement: 96 eyes with full agreement, 28 eyes with disagreement. The kappa value for concordance on "whether to refer" was 0.70 (95% CI 0.60-0.80), indicating substantial agreement. Using the board-certified retina specialist as the gold standard, EyeArt had an 81% accuracy (101/124 eyes) for diagnosis and 83% accuracy (103/124 eyes) in referrals. For referrals, the sensitivity of EyeArt was 74%, specificity was 87%, positive predictive value was 72%, and negative predictive value was 88%.
This retrospective cross-sectional analysis offers insights into use of AI in diabetic screenings and the significant role it will play in automated detection of DR. The EyeArt readings were beneficial with some limitations in a community screening environment. These limitations included a decreased accuracy in the presence of cataracts and the functional cost of EyeArt uploads in a community setting.
糖尿病视网膜病变（DR）的早期检测和治疗对于降低视力丧失风险和预防失明至关重要。社区视力筛查可能发挥重要作用，尤其是在糖尿病风险较高的社区。为满足增加DR检测和转诊的需求，我们评估了人工智能（AI）在DR筛查中的应用。
在内分泌科诊所（新泽西州纽瓦克）和一次社区筛查活动（新泽西州纽瓦克）中，使用佳能45°非散瞳CR-2 Plus AF视网膜相机获取了124只眼睛的患者图像。图像最初由现场分级人员进行分类，然后上传至EyeArt进行分析，EyeArt是由美国加利福尼亚州的Eyenuk公司开发的基于云的AI软件。这些图像还由一位非现场的视网膜专家进行分级。使用Fleiss卡方分析，研究了三种分级系统（AI、现场分级人员和美国董事会认证的视网膜专家）在DR诊断和转诊模式方面的相关性。
EyeArt的结果、现场分级人员和视网膜专家在DR诊断上的总体一致性为79%：86只眼睛完全一致，37只眼睛在两位分级人员之间一致，1只眼睛完全不一致。诊断一致性的卡方值为0.69（95%可信区间0.61 - 0.77），表明有实质性一致性。EyeArt、现场分级人员和眼科医生的转诊模式总体一致性为85%：96只眼睛完全一致，28只眼睛不一致。“是否转诊”一致性的卡方值为0.70（95%可信区间0.60 - 0.80），表明有实质性一致性。以董事会认证的视网膜专家作为金标准，EyeArt在诊断方面的准确率为81%（101/124只眼睛），在转诊方面的准确率为83%（103/124只眼睛）。对于转诊，EyeArt的敏感性为74%，特异性为87%，阳性预测值为72%，阴性预测值为88%。
这项回顾性横断面分析为AI在糖尿病筛查中的应用以及它在DR自动检测中将发挥的重要作用提供了见解。在社区筛查环境中，EyeArt的读数有一定益处，但也存在一些局限性。这些局限性包括在存在白内障时准确性降低以及在社区环境中上传EyeArt的功能成本。
