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

Autor: Chia-Jung Liu, Cheng Che Tsai, Lu-Cheng Kuo, Po-Chih Kuo, Meng-Rui Lee, Jann-Yuan Wang, Jen-Chung Ko, Jin-Yuan Shih, Hao-Chien Wang, Chong-Jen Yu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Insights into Imaging, Vol 14, Iss 1, Pp 1-12 (2023)
Druh dokumentu: article
ISSN: 1869-4101
DOI: 10.1186/s13244-023-01395-9
Popis: 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 curves (AUC) in both internal and external test sets. Furthermore, we conducted a reader study and tested our model under three scenarios of different mycobacteria prevalence. Results Among the internal and external test sets, the AUCs of our DNN model were 0.83 ± 0.005 and 0.76 ± 0.006 for pulmonary TB, 0.86 ± 0.006 and 0.64 ± 0.017 for NTM-LD, and 0.77 ± 0.007 and 0.74 ± 0.005 for Imitator. The DNN model showed higher performance on the internal test set in classification accuracy (66.5 ± 2.5%) than senior (50.8 ± 3.0%, p
Databáze: Directory of Open Access Journals
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