在秘鲁利马,使用人工智能软件作为分诊和筛查工具对住院结核病患者进行数字胸部x光分析的准确性。

Amanda Biewer, Christine Tzelios, Karen Tintaya, Betsabe Roman, Shelley Hurwitz, Courtney M Yuen, Carole D Mitnick, Edward Nardell, Leonid Lecca, Dylan B Tierney, Ruvandhi R Nathavitharana
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摘要

简介:结核病在医疗机构的传播在高发病率国家很常见。然而,确定可能患有结核病的住院患者的最佳方法尚不清楚。我们评估了qXR(Qure.ai,印度)计算机辅助检测(CAD)软件版本3和4(v3和v4)作为FAST(主动发现病例、安全分离和有效治疗)传播控制策略中的分诊和筛查工具的诊断准确性。方法:我们前瞻性地招募了两组入住秘鲁利马三级医院的患者:一组有咳嗽或结核病危险因素(分诊),另一组没有报告咳嗽或结核病风险因素(筛查)。我们使用培养和Xpert作为主要和次要参考标准,评估了qXR诊断肺结核的敏感性和特异性,包括基于风险因素的分层分析。结果:在分诊队列(n=387)中,使用培养物作为参考标准,qXRv4的敏感性为0.95(62/65,95%CI 0.87-0.99),特异性为0.36(116/322,95%CI 0.31-0.42)。在培养物或Xpert参考标准的qXRv3和qxRv4之间,受试者工作特性曲线下面积(AUC)没有差异。在筛查队列(n=191)中,只有一名患者的Xpert结果呈阳性,但该队列的特异性很高(>90%)。qXR敏感性按性别、年龄、既往结核病、HIV和症状分层没有差异。结论:qXR在有咳嗽或结核病危险因素的住院患者中具有高灵敏度,但特异性低。在这种情况下筛查没有咳嗽的患者的诊断率较低。这些发现进一步支持了对CAD程序的总体需求和设置特定阈值的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru.

Introduction: Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy.

Methods: We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors.

Results: In the triage cohort (n=387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n=191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort.

Conclusions: qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.

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