Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru

Abstract

Introduction Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3 and 4 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. Methods We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. Results In the triage cohort (n=387), qXRv4 sensitivity was 0.95 (62/65, 95% CI 0.87-0.99) and specificity was 0.36 (116/322, 95% CI 0.31-0.42) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXRv3 and qxRv4 with either a culture or Xpert reference standard. In the screening cohort (n=191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). qXR sensitivity did not differ stratified by sex, age, prior TB, HIV, and symptoms. Specificity was higher in people without prior TB and people with a cough for <2 weeks. Conclusions qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.