A deep learning model using chest X-ray for identifying TB and NTM-LD patients: a cross-sectional study.

ABSTRACT:

Background

Timely differentiating between pulmonary tuberculosis (TB) and nontuberculous mycobacterial lung disease (NTM-LD), which are radiographically similar, is important because infectiousness and treatment differ. This study aimed to evaluate whether artificial intelligence could distinguish between TB or NTM-LD patients by chest X-rays (CXRs) from suspects of mycobacterial lung disease.

Methods

A total of 1500 CXRs, including 500 each from patients with pulmonary TB, NTM-LD, and patients with clinical suspicion but negative mycobacterial culture (Imitator) from two hospitals, were retrospectively collected and evaluated in this study. We developed a deep neural network (DNN) and evaluated model performance using the area under the receiver operating characteristic cur

SUBMITTER: Liu CJ 

PROVIDER: S-EPMC10105818 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature