Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis.

ABSTRACT: 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, an

SUBMITTER: Nijiati M 

PROVIDER: S-EPMC9760807 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature