Published 29 Dec 2021

Evaluation of chest X-Ray with automated interpretation algorithms for mass tuberculosis screening in prisons



The World Health Organization (WHO) recommends systematic tuberculosis (TB) screening in prisons. Evidence is lacking for accurate and scalable screening approaches in this setting. 
To assess the diagnostic accuracy of artificial intelligence-based chest x-ray interpretation algorithms for TB screening in prisons. 
Prospective TB screening study in three prisons in Brazil from October 2017 to December 2019. We administered a standardized questionnaire, performed chest x-ray in a mobile unit, and collected sputum for confirmatory testing using Xpert MTB/RIF and culture. We evaluated x-ray images using three algorithms (CAD4TB version 6, LunitTB and qXR) and compared their diagnostic accuracy. We utilized multivariable logistic regression to assess the effect of demographic and clinical characteristics on algorithm accuracy. Finally, we investigated the relationship between abnormality scores and Xpert semi-quantitative results. 
Measurements and Main Results
Among 2,075 incarcerated individuals, 259 (12.5%) had confirmed TB. All three algorithms performed similarly overall with AUCs of 0.87-0.91. At 90% sensitivity, only LunitTB and qXR met the WHO Target Product Profile requirements for a triage test, with specificity of 84% and 74%, respectively. All algorithms had variable performance by age, prior TB, smoking, and presence of TB symptoms. LunitTB was the most robust to this heterogeneity, but nonetheless failed to meet the TPP for individuals with previous TB. Abnormality scores of all three algorithms were significantly correlated with sputum bacillary load. 
Automated x-ray interpretation algorithms can be an effective triage tool for TB screening in prisons. However, their specificity is insufficient in individuals with previous TB. 



1. Faculty of Health Sciences of Federal University of Grande Dourados 2. Dourados 3. Mato Grosso do Sul 4. Brazil Nursing School 5. State University of Mato Grosso do Sul 6. Dourados 7. MS 8. Brazil Stanford University School of Medicine 9. Division of Infectious Diseases and Geographic Medicine 10. Stanford 11. CA 12. United States of America Oswaldo Cruz Foundation 13. Mato Grosso do Sul 14. Campo Grande 15. MS 16. Brazil Department of Radiology 17. Seoul National University College of Medicine 18. Seoul 19. Korea Department of Radiology 20. Seoul National University Hospital 21. Seoul 22. Korea Department of Epidemiology of Microbial Diseases 23. Yale University School of Public Health 24. New Haven 25. United States of America

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