Sarao Valentina, Veritti Daniele, Lanzetta Paolo
Department of Medicine-Ophthalmology, University of Udine, Via Colugna 50, 33100, Udine, Italy.
Istituto Europeo di Microchirurgia Oculare-IEMO, Udine, Italy.
Graefes Arch Clin Exp Ophthalmol. 2020 Dec;258(12):2647-2654. doi: 10.1007/s00417-020-04853-y. Epub 2020 Sep 16.
In this study, we evaluated the diagnostic performance of an automated artificial intelligence-based diabetic retinopathy (DR) algorithm with two retinal imaging systems using two different technologies: a conventional flash fundus camera and a white LED confocal scanner.
On the same day, patients underwent dilated colour fundus photography using both a conventional flash fundus camera (TRC-NW8, Topcon Corporation, Tokyo, Japan) and a fully automated white LED confocal scanner (Eidon, Centervue, Padova, Italy). All images were analysed for DR severity both by retina specialists and the AI software EyeArt (Eyenuk Inc., Los Angeles, CA) and graded as referable DR (RDR) or not RDR. Sensitivity, specificity and the area under the curve (AUC) were computed.
A series of 165 diabetic subjects (330 eyes) were enrolled. The automated algorithm achieved 90.8% sensitivity with 75.3% specificity on images acquired with the conventional fundus camera and 94.1% sensitivity with 86.8% specificity on images obtained from the white LED confocal scanner. The difference between AUC was 0.0737 (p = 0.0023).
The automated image analysis software is well suited to work with different imaging technologies. It achieved a better diagnostic performance when the white LED confocal scanner is used. Further evaluation in the context of screening campaigns is needed.
在本研究中，我们使用两种不同技术的视网膜成像系统，评估了基于人工智能的糖尿病视网膜病变（DR）自动算法的诊断性能：传统闪光眼底照相机和白色发光二极管共聚焦扫描仪。
同一天，患者使用传统闪光眼底照相机（TRC-NW8，拓普康公司，东京，日本）和全自动白色发光二极管共聚焦扫描仪（Eidon，Centervue，帕多瓦，意大利）进行散瞳彩色眼底摄影。视网膜专家和人工智能软件EyeArt（Eyenuk公司，洛杉矶，加利福尼亚）对所有图像进行DR严重程度分析，并分级为可参考性DR（RDR）或非RDR。计算敏感性、特异性和曲线下面积（AUC）。
纳入了165名糖尿病患者（330只眼）。自动算法在使用传统眼底照相机获取的图像上，敏感性达到90.8%，特异性为75.3%；在使用白色发光二极管共聚焦扫描仪获得的图像上，敏感性为94.1%，特异性为86.8%。AUC之间的差异为0.0737（p = 0.0023）。
自动图像分析软件非常适合与不同的成像技术配合使用。使用白色发光二极管共聚焦扫描仪时，其诊断性能更佳。需要在筛查活动背景下进行进一步评估。
