Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs.

Authors

Munjal P,Mahrooqi AA,Rajan R,Jeremijenko A,Ahmad I,Akhtar MI,Pimentel MAF,Khan S

Affiliations (3)

  • M42, Abu Dhabi, UAE. [email protected].
  • M42, Abu Dhabi, UAE.
  • Capital Health Screening Centre, Abu Dhabi, UAE.

Abstract

Traditional tuberculosis (TB) screening involves radiologists manually reviewing chest X-rays (CXR), which is time-consuming, error-prone, and limited by workforce shortages. Our AI model, AIRIS-TB (AI Radiology In Screening TB), aims to address these challenges by automating the reporting of all X-rays without any findings. AIRIS-TB was evaluated on over one million CXRs, achieving an AUC of 98.51% and overall false negative rate (FNR) of 1.57%, outperforming radiologists (1.85%) while maintaining a 0% TB-FNR. By selectively deferring only cases with findings to radiologists, the model has the potential to automate up to 80% of routine CXR reporting. Subgroup analysis revealed insignificant performance disparities across age, sex, HIV status, and region of origin, with sputum tests for suspected TB showing a strong correlation with model predictions. This large-scale validation demonstrates AIRIS-TB's safety and efficiency in high-volume TB screening programs, reducing radiologist workload without compromising diagnostic accuracy.

Topics

Journal Article

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