AI-assisted differentiation of nontuberculous mycobacterial pulmonary disease from colonization: a multi-center study.
Authors
Affiliations (8)
Affiliations (8)
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan.
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
- Department of Internal Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan.
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan. [email protected].
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan. [email protected].
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Abstract
Differentiating between nontuberculous mycobacteria (NTM) pulmonary disease (NTM-PD) and colonization (NTM-PC) is clinically important but difficult. It remains unknown whether artificial intelligence utilizing clinical data and chest CT images could address this clinical problem. Patients were retrospectively recruited with NTM isolation from respiratory specimens in two hospitals. Their disease or colonization status was determined by three NTM experts. We developed a multimodal deep learning model named NTMNet, which integrates chest CT scans and clinical data (including age, sex, acid-fast smear [AFS] results, and mycobacterial species) to predict NTM disease status. The performance of NTMNet was evaluated on both internal and external test sets. A total of 324 NTM-PC patients and 285 NTM-PD patients were included. Among the internal and external test sets, the area under the receiver operating characteristic curve (AUC) for predicting NTM disease status using CT imaging was 0.73 (95% CI: 0.62-0.82) and 0.78 (95% CI: 0.75-0.83), respectively. When imaging data were integrated with clinical information, our NTMNet model achieved AUC values of 0.85 (95% CI: 0.80-0.93) and 0.82 (95% CI: 0.78-0.89), respectively. Furthermore, our NTMNet model demonstrated comparable accuracy to that of three experienced pulmonologists in determining NTM disease status in the reader study. Our multimodal NTMNet exhibited satisfactory performance in distinguishing disease status among patients with respiratory NTM isolates. This deep learning-based model has the potential to assist physicians in clinical management, achieving diagnostic accuracy comparable to that of pulmonologists. A deep learning model leveraging chest computed tomography images and clinical data effectively differentiated NTM disease status, achieving a classification accuracy comparable to that of pulmonologists and demonstrating its potential to support accurate NTM diagnosis in clinical settings. Accurately distinguishing nontuberculous mycobacteria (NTM) disease status is clinically important but challenging. The NTMNet model effectively differentiated the NTM disease status and matched the performance of the pulmonologists. The NTMNet model could be a potential diagnostic tool for patients with respiratory NTM isolates.