A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

The authors of this study aimed to develop an artificial intelligence (AI)-based fully automated CT image analysis system in order to detect and diagnose pulmonary tuberculosis (TB). This was achieved through the retrospective use of 892 chest CT scans from pathogen-confirmed TB patients. It was found that the end-to-end AI system based on chest CT is able to achieve human-level diagnostic performance in the early detection and clinical management of patients with pulmonary TB.

Key points

• Deep learning allows automatic detection, diagnosis, and evaluation of pulmonary tuberculosis.
• Artificial intelligence helps clinicians to assess patients with tuberculosis.
• Pulmonary tuberculosis disease activity and treatment management can be improved.

Article: A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

Authors: Chenggong Yan, Lingfeng Wang, Jie Lin, Jun Xu, Tianjing Zhang, Jin Qi, Xiangying Li, Wei Ni, Guangyao Wu, Jianbin Huang, Yikai Xu, Henry C. Woodruff & Philippe Lambin

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European Radiology