Kapetanakis Venediktos V, Rudnicka Alicja R, Liew Gerald, Owen Christopher G, Lee Aaron, Louw Vern, Bolter Louis, Anderson John, Egan Catherine, Salas-Vega Sebastian, Rudisill Caroline, Taylor Paul, Tufail Adnan
Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, United Kingdom
Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, United Kingdom.
J Med Screen. 2015 Sep;22(3):112-8. doi: 10.1177/0969141315571953. Epub 2015 Mar 5.
Diabetic retinopathy screening in England involves labour intensive manual grading of digital retinal images. We present the plan for an observational retrospective study of whether automated systems could replace one or more steps of human grading.
Patients aged 12 or older who attended the Diabetes Eye Screening programme, Homerton University Hospital (London) between 1 June 2012 and 4 November 2013 had macular and disc-centred retinal images taken. All screening episodes were manually graded and will additionally be graded by three automated systems. Each system will process all screening episodes, and screening performance (sensitivity, false positive rate, likelihood ratios) and diagnostic accuracy (95% confidence intervals of screening performance measures) will be quantified. A sub-set of gradings will be validated by an approved Reading Centre. Additional analyses will explore the effect of altering thresholds for disease detection within each automated system on screening performance.
2,782/20,258 diabetes patients were referred to ophthalmologists for further examination. Prevalence of maculopathy (M1), pre-proliferative retinopathy (R2), and proliferative retinopathy (R3) were 7.9%, 3.1% and 1.2%, respectively; 4749 (23%) patients were diagnosed with background retinopathy (R1); 1.5% were considered ungradable by human graders.
Retinopathy prevalence was similar to other English diabetic screening programmes, so findings should be generalizable. The study population size will allow the detection of differences in screening performance between the human and automated grading systems as small as 2%. The project will compare performance and economic costs of manual versus automated systems.
在英国,糖尿病视网膜病变筛查涉及对数字视网膜图像进行劳动强度大的人工分级。我们提出了一项观察性回顾性研究计划,以探讨自动化系统是否可以取代人工分级的一个或多个步骤。
2012年6月1日至2013年11月4日期间,在伦敦哈默顿大学医院参加糖尿病眼部筛查项目的12岁及以上患者拍摄了黄斑和以视盘为中心的视网膜图像。所有筛查记录均进行了人工分级,并将另外由三个自动化系统进行分级。每个系统将处理所有筛查记录,并对筛查性能(灵敏度、假阳性率、似然比)和诊断准确性(筛查性能指标的95%置信区间)进行量化。一部分分级将由一个经批准的阅读中心进行验证。额外的分析将探讨在每个自动化系统内改变疾病检测阈值对筛查性能的影响。
2782/20258例糖尿病患者被转诊至眼科医生处进行进一步检查。黄斑病变(M1)、增殖前期视网膜病变(R2)和增殖性视网膜病变(R3)的患病率分别为7.9%、3.1%和1.2%;4749例(23%)患者被诊断为背景性视网膜病变(R1);1.5%的患者被人工分级人员认为无法分级。
视网膜病变患病率与英国其他糖尿病筛查项目相似,因此研究结果应具有普遍性。研究人群规模将能够检测出人工分级系统和自动化分级系统之间低至2%的筛查性能差异。该项目将比较人工系统和自动化系统的性能及经济成本。