Using AI system to detect active tuberculosis in a high-prevalence setting on CT scans: a multi-center study

To evaluate the feasibility of an AI system for identifying active tuberculosis (ATB) in TB-specialized hospitals in high-prevalence settings. An AI system designed to identify ATB was retrospectively validated using a multi-center dataset of 1741 CT images from three TB-specialized hospitals. The dataset included ATB, pneumonia, pulmonary nodules and normal cases. The system’s utility and generalizability were assessed across four application scenarios, and pairwise comparisons of the system’s performance were conducted among the three hospitals. The system demonstrated good generalizability across three settings. It achieved an AUC over 0.9 for distinguishing between abnormal and normal, over 0.95 for distinguishing between ATB and normal, over 0.8 for distinguishing between ATB and non-ATB, and an AUC ranging from 0.762 to 0.906 for distinguishing between ATB and other abnormalities (pneumonia and pulmonary nodules). For all evaluation matrices, at least one pairwise comparison showed no significant difference in performance among the three hospitals across different scenarios. Using an AI system to identify ATB in CT images is feasible in TB-specialized hospitals. This evaluation provides valuable insights for those looking to implement AI to support clinical decision-making and optimize resource utilization in hospitals overwhelmed by TB cases.

• Publication year

  2025

• Venue

  Scientific Reports

• Publication date

  2025-11-11

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1038/s41598-025-23172-yPMID 41219350PMCID 12606113
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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