Rahman Md Toufiq, Codlin Andrew J, Rahman Md Mahfuzur, Nahar Ayenun, Reja Mehdi, Islam Tariqul, Qin Zhi Zhen, Khan Md Abdus Shakur, Banu Sayera, Creswell Jacob
International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh.
Stop TB Partnership, Geneva, Switzerland.
Eur Respir J. 2017 May 21;49(5). doi: 10.1183/13993003.02159-2016. Print 2017 May.
Computer-aided reading (CAR) of medical images is becoming increasingly common, but few studies exist for CAR in tuberculosis (TB). We designed a prospective study evaluating CAR for chest radiography (CXR) as a triage tool before Xpert MTB/RIF (Xpert).Consecutively enrolled adults in Dhaka, Bangladesh, with TB symptoms received CXR and Xpert. Each image was scored by CAR and graded by a radiologist. We compared CAR with the radiologist for sensitivity and specificity, area under the receiver operating characteristic curve (AUC), and calculated the potential Xpert tests saved.A total of 18 036 individuals were enrolled. TB prevalence by Xpert was 15%. The radiologist graded 49% of CXRs as abnormal, resulting in 91% sensitivity and 58% specificity. At a similar sensitivity, CAR had a lower specificity (41%), saving fewer (36%) Xpert tests. The AUC for CAR was 0.74 (95% CI 0.73-0.75). CAR performance declined with increasing age. The radiologist grading was superior across all sub-analyses.Using CAR can save Xpert tests, but the radiologist's specificity was superior. Differentiated CAR thresholds may be required for different populations. Access to, and costs of, human readers must be considered when deciding to use CAR software. More studies are needed to evaluate CAR using different screening approaches.
医学图像的计算机辅助阅读(CAR)正变得越来越普遍,但针对结核病(TB)的计算机辅助阅读研究却很少。我们设计了一项前瞻性研究,评估将胸部X光摄影(CXR)的计算机辅助阅读作为Xpert MTB/RIF(Xpert)检测前的分诊工具。在孟加拉国达卡,连续招募有结核病症状的成年人,他们接受了胸部X光摄影和Xpert检测。每张图像由计算机辅助阅读进行评分,并由放射科医生进行分级。我们将计算机辅助阅读与放射科医生在敏感性和特异性、受试者操作特征曲线下面积(AUC)方面进行比较,并计算节省的潜在Xpert检测次数。
总共招募了18036人。Xpert检测出的结核病患病率为15%。放射科医生将49%的胸部X光片评为异常,敏感性为91%,特异性为58%。在相似的敏感性下,计算机辅助阅读的特异性较低(41%),节省的Xpert检测次数较少(36%)。计算机辅助阅读的AUC为0.74(95%可信区间0.73 - 0.75)。计算机辅助阅读的性能随着年龄增长而下降。在所有亚分析中,放射科医生的分级表现更优。
使用计算机辅助阅读可以节省Xpert检测次数,但放射科医生的特异性更优。不同人群可能需要不同的计算机辅助阅读阈值。在决定使用计算机辅助阅读软件时,必须考虑人力阅读者的可及性和成本。需要更多研究使用不同的筛查方法来评估计算机辅助阅读。