Advances in research on chest CT combined with artificial intelligence-assisted detection in the diagnosis and treatment of pulmonary tuberculosis

doi:10.19983/j.issn.2096-8493.20250134

Abstract

Abstract:

Early accurate diagnosis and drug resistance identification play a crucial role in the prevention and control of pulmonary tuberculosis (PTB). Conventional laboratory tests have significant lag, while manual interpretation of chest imaging is prone to interference from subjective factors. However, artificial intelligence (AI) technology provides a new direction for optimizing PTB diagnosis and treatment process. At present, chest X-ray computer-aided detection (CXR-CAD) software has been applied in daily clinical work. For computed tomography computer-aided detection (CT-CAD), relevant studies have established a variety of AI models and conducted clinical tests in the fields of PTB differential diagnosis, effectiveness, drug resistance prediction, and scientific research assistance. These models have demonstrated important application value in PTB diagnosis and treatment practice. This study reviews the research progress of CT-CAD in the above-mentioned fields, analyzes bottleneck problems in its clinical transformation process, to provide a scientific basis for the efficient application of CT-CAD in PTB diagnosis and treatment.

Key words: Tuberculosis, pulmonary, Tomography, Artificial intelligence, Diagnosis

CLC Number:

Zhang Jiacheng, Tang Shenjie, Hou Dailun, Li Liang. Advances in research on chest CT combined with artificial intelligence-assisted detection in the diagnosis and treatment of pulmonary tuberculosis[J]. Journal of Tuberculosis and Lung Disease , 2026, 7(1): 112-119. doi: 10.19983/j.issn.2096-8493.20250134

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