在秘鲁利马，将人工智能软件用于对疑似结核病住院患者进行分诊和筛查时，数字胸部X光分析的准确性。

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.

作者信息

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

机构信息

Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA.

Harvard Medical School, Boston, MA.

出版信息

medRxiv. 2023 Dec 7:2023.05.17.23290110. doi: 10.1101/2023.05.17.23290110.

DOI:10.1101/2023.05.17.23290110

PMID:37292955

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10246158/

Abstract

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.

摘要

引言

在结核病高发病率国家，医疗机构内的结核病传播很常见。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（印度Qure.ai公司）计算机辅助检测（CAD）软件版本3.0和4.0（v3和v4）作为FAST（积极发现病例、安全隔离和有效治疗）传播控制策略中的分诊和筛查工具的诊断准确性。

方法

我们前瞻性地纳入了秘鲁利马一家三级医院收治的两组患者：一组有咳嗽或结核病风险因素（分诊），另一组未报告咳嗽或结核病风险因素（筛查）。我们以培养和Xpert作为主要和次要参考标准，评估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，以培养为参考标准时，qXR v4的敏感性为0.91（59/65，95%CI 0.81-0.97），特异性为0.32（103/322，95%CI 0.27-0.37）。以培养或Xpert为参考标准时，qXR v3和qXR v4的受试者操作特征曲线下面积（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，但该队列中的特异性很高（>90%）。通过qXR在分诊队列的数字胸部X光图像上检测到肺部影像学异常的患病率很高，最显著的是不透明度（81%）、实变（62%）或结节（58%）。