Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK
University College London Institute of Ophthalmology, London, UK.
Br J Ophthalmol. 2021 Feb;105(2):265-270. doi: 10.1136/bjophthalmol-2019-315394. Epub 2020 May 6.
Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading.
Cross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images.
We included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images.
EyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of 'no retinopathy' and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting.
照相法糖尿病视网膜病变筛查需要人力对视网膜图像进行分级。自动视网膜图像分析软件（ARIAS）可提供一种替代人工分级的方法。我们比较了在英国国家糖尿病眼病筛查计划（NDESP）中使用真彩色、广角共焦扫描图像和标准眼底图像的 ARIAS 与人工分级的性能。
这项横断面研究连续招募了参加年度糖尿病眼病筛查的患者。使用 EIDON 平台（意大利帕多瓦的 CenterVue）和标准 NDESP 相机进行散瞳双视野成像。根据 NDESP 方案进行人工分级。图像由 EyeArt V.2.1.0（Eyenuk Inc，加利福尼亚州伍德兰希尔斯）处理。分析的参考标准是标准 NDESP 图像的人工分级。
我们纳入了 1257 名患者。视网膜病变分级的敏感性估计值为：EIDON 图像：任何视网膜病变为 92.27%（95%CI：88.43%至 94.69%），威胁视力的视网膜病变为 99%（95%CI：95.35%至 100%），增殖性视网膜病变为 100%（95%CI：61%至 100%）。对于 NDESP 图像：任何视网膜病变为 92.26%（95%CI：88.37%至 94.69%），威胁视力的视网膜病变为 100%（95%CI：99.53%至 100%），增殖性视网膜病变为 100%（95%CI：61%至 100%）。当分析 EIDON 图像时，EyeArt 漏诊了 1 例威胁视力的视网膜病变（R1M1），但被人工分级识别出来。EyeArt 在标准图像中识别出了所有威胁视力的视网膜病变病例。
在大规模筛查项目中，EyeArt 用 EIDON 图像识别糖尿病视网膜病变的敏感性与标准图像相似，超过了筛查试验推荐的敏感性阈值。进一步优化“无视网膜病变”的识别和理解两种成像系统的差异病变检测，将增强这两种创新技术在糖尿病视网膜病变筛查中的应用。
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