A systematic review and meta-analysis of artificial intelligence software for tuberculosis diagnosis using chest X-ray imaging.

Journal: Journal of thoracic disease
Published Date: May 27, 2025

Abstract

BACKGROUND: Pulmonary tuberculosis (PTB) remains a global public health challenge, with 10.8 million new cases reported in 2023. Early diagnosis is crucial for controlling its spread, yet traditional sputum-based tests face limitations in turnaround time and resource availability. Chest X-ray (CXR) is a cost-effective diagnostic tool, but its use in high-tuberculosis (TB) burden regions is restricted by a shortage of radiologists. Artificial intelligence (AI)-based computer-aided detection (CAD) systems, leveraging deep learning, offer a promising solution for automated PTB detection. However, variability in diagnostic performance across AI tools and the need for scenario-specific threshold adjustments remain challenges that need to be addressed. Our meta-analysis evaluated the diagnostic accuracy of five AI-based PTB detection products, aiming to provide insights for advancing AI applications in TB screening and diagnosis.

Authors

  • Zhi-Lin Han
    Department of Radiology, Haihe Hospital, Tianjin University, Tianjin, China.
  • Yu-Yang Zhang
    Haihe Clinical School, Tianjin Medical University, Tianjin, China.
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Shan Gao
    Department of Mathematics and Statistics, Yunnan University, China.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
  • Wan-Jie Yang
    Tianjin Institute of Respiratory Diseases, Tianjin, China.
  • Zhi-Heng Xing
    Department of Radiology, Haihe Hospital, Tianjin University, Tianjin, China.

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