Nørgaard Mads Fonager, Grauslund Jakob
Department of Ophthalmology, Odense University Hospital, Odense, Denmark.
Research Unit of Ophthalmology, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.
Ophthalmic Res. 2018;60(1):9-17. doi: 10.1159/000486284. Epub 2018 Jan 16.
Worldwide ophthalmologists are challenged by the rapid rise in the prevalence of diabetes. Diabetic retinopathy (DR) is the most common complication in diabetes, and possible consequences range from mild visual impairment to blindness. Repetitive screening for DR is cost-effective, but it is also a costly and strenuous affair. Several studies have examined the application of automated image analysis to solve this problem. Large populations are needed to assess the efficacy of such programs, and a standardized and rigorous methodology is important to give an indication of system performance in actual clinical settings.
In a systematic review, we aimed to identify studies with methodology and design that are similar or replicate actual screening scenarios. A total of 1,231 publications were identified through PubMed, Cochrane Library, and Embase searches. Three manual search strategies were carried out to identify publications missed in the primary search. Four levels of screening identified 7 studies applicable for inclusion.
Seven studies were included. The detection of DR had high sensitivities (87.0-95.2%) but lower specificities (49.6-68.8%). False-negative results were related to mild DR with a low risk of progression within 1 year. Several studies reported missed cases of diabetic macular edema. A meta-analysis was not conducted as studies were not suitable for direct comparison or statistical analysis.
The study demonstrates that despite limited specificity, automated retinal image analysis may potentially be valuable in different DR screening scenarios with a relatively high sensitivity and a substantial workload reduction.
全球眼科医生面临着糖尿病患病率迅速上升的挑战。糖尿病视网膜病变（DR）是糖尿病最常见的并发症，其可能后果从轻度视力损害到失明不等。对DR进行重复筛查具有成本效益，但也是一项昂贵且费力的工作。多项研究探讨了应用自动图像分析来解决这一问题。需要大量人群来评估此类程序的有效性，并且标准化且严格的方法对于表明系统在实际临床环境中的性能很重要。
在一项系统评价中，我们旨在识别方法和设计与实际筛查场景相似或重复的研究。通过PubMed、Cochrane图书馆和Embase检索共识别出1231篇出版物。开展了三种手动检索策略以识别在初步检索中遗漏的出版物。四级筛选确定了7项适用于纳入的研究。
纳入了7项研究。DR检测具有较高的敏感性（87.0 - 95.2%）但特异性较低（49.6 - 68.8%）。假阴性结果与1年内进展风险较低的轻度DR有关。多项研究报告了糖尿病黄斑水肿的漏诊病例。由于研究不适合直接比较或统计分析，因此未进行荟萃分析。
该研究表明，尽管特异性有限，但自动视网膜图像分析在不同的DR筛查场景中可能具有潜在价值，具有相对较高的敏感性且可大幅减少工作量。
