Active pulmonary tuberculosis (ATB), which is more infectious and has a higher mortality rate compared with non-active pulmonary tuberculosis (non-ATB), needs to be diagnosed accurately and timely to prevent the tuberculosis from spreading and causing deaths. However, traditional differential diagnosis methods of active pulmonary tuberculosis involve bacteriological testing, sputum culturing and radiological images reading, which is time consuming and labour intensive. Therefore, an artificial intelligence model for ATB differential diagnosis would offer great assistance in clinical practice. In this study, computer tomography (CT) scans images and corresponding clinical information of 1160 ATB patients and 1131 patients with non-ATB were collected and divided into training, validation, and testing sets. A 3-dimension (3D) Nested UNet model was utilized to delineate lung field regions in the CT images, and three different pre-trained deep learning models including 3D VGG-16, 3D EfficientNet and 3D ResNet-50 were used for classification and differential diagnosis task. We also collected an external testing set with 100 ATB cases and 100 Non-ATB cases for further validation of the model. In the internal and external testing set, the 3D ResNet-50 model outperformed other models, reaching an AUC of 0.961 and 0.946, respectively. The 3D ResNet-50 model reached even higher levels of diagnostic accuracy than experienced radiologists, while the CT images reading and diagnosing speed was 10 times faster than human experts. The model was also capable of visualizing clinician interpretable lung lesion regions important for differential diagnosis, making it a powerful tool assisting ATB diagnosis. In conclusion, we developed an auxiliary tool to differentiate active and non-active pulmonary tuberculosis, which would have broad prospects in the bedside.

Objective: To develop a CT image-based deep learning framework (3D-ResNet) as a new technology for the early warning of active and secondary pulmonary tuberculosis.Methods: Chest CT images of patients with active pulmonary tuberculosis (n=1,160) and secondary pulmonary tuberculosis (n=1,131) diagnosed via bacteriological examination were retrospectively collected to analyze differences between the clinical and imaging presentations of the two diseases. Lung field regions were presegmented by using a 3D Nested UNet model, and a 3D-ResNet model was developed for training the classification model. The data were randomly grouped at a ratio of 7:2:1 for training, validation and testing. Area under the curve (AUC), accuracy (ACC), recall and F1 scores were used as model evaluation metrics, and class activation mapping were used to evaluate the activation regions of interest.Results: Patients with active pulmonary tuberculosis were older than those with secondary pulmonary tuberculosis. Additionally, patients with active pulmonary tuberculosis were observed to cough more, and patients with secondary pulmonary tuberculosis experienced more chest pain. The differences were statistically significant (p<0.05). The AUC values of the model on the validation and test datasets were 0.948 and 0.945, respectively, with ACC values being 0.973 and 0.969, Recall values being 0.941 and 0.949 and F1 scores being 0.977 and 0.969, respectively. Conclusion: This study demonstrated that 3D-ResNet can be used as a rapid auxiliary diagnostic tool for differentiating active pulmonary tuberculosis and secondary pulmonary tuberculosis, which can help patients with active pulmonary tuberculosis in receiving timely treatment, in reducing transmission and in avoiding overtreatment. Additionally, this tool can aid secondary pulmonary tuberculosis patients who are misdiagnosed as having active pulmonary tuberculosis.