Evaluation of chest X-ray with automated interpretation algorithms for mass tuberculosis screening in prisons: a cross-sectional study

Thiego Ramon Soares; Roberto Dias de Oliveira; Yiran E Liu; Andrea da Silva Santos; Paulo Cesar Pereira Dos Santos; Luma Ravena Soares Monte; Lissandra Maia de Oliveira; Chang Min Park; Eui Jin Hwang; Jason R Andrews; Julio Croda

doi:10.1016/j.lana.2022.100388

Evaluation of chest X-ray with automated interpretation algorithms for mass tuberculosis screening in prisons: a cross-sectional study

Lancet Reg Health Am. 2022 Nov 4:17:100388. doi: 10.1016/j.lana.2022.100388. eCollection 2023 Jan.

Authors

Affiliations

¹ Faculty of Health Sciences of Federal University of Grande Dourados, Dourados, MS, Brazil.
² Nursing School, State University of Mato Grosso do Sul, Dourados, MS, Brazil.
³ Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, United States of America.
⁴ Oswaldo Cruz Foundation, Campo Grande, MS, Brazil.
⁵ Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
⁶ Department of Radiology, Seoul National University Hospital, Seoul, Korea.
⁷ Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, CT, United States of America.
⁸ School of Medicine, Federal University of Mato Grosso do Sul, Campo Grande, MS, Brazil.

Abstract

Background: The World Health Organization (WHO) recommends systematic tuberculosis (TB) screening in prisons. Evidence is lacking for accurate and scalable screening approaches in this setting. We aimed to assess the accuracy of artificial intelligence-based chest x-ray interpretation algorithms for TB screening in prisons.

Methods: We performed prospective TB screening in three male prisons in Brazil from October 2017 to December 2019. We administered a standardized questionnaire, performed a 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, Lunit version 3.1.0.0 and qXR version 3) and compared their 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.

Findings: Among 2075 incarcerated individuals, 259 (12.5%) had confirmed TB. All three algorithms performed similarly overall with area under the receiver operating characteristic curve (AUC) of 0.88-0.91. 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.

Interpretation: 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.

Funding: This study was supported by the US National Institutes of Health (grant numbers R01 AI130058 and R01 AI149620) and the State Secretary of Health of Mato Grosso do Sul.

Keywords: Automated interpretation; Diagnostics; Prisons; Tuberculosis; X-ray.

Abstract

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