Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard. The area under the curve of the three systems was similar: Lunit (0.94, 95% CI: 0.93–0.96), qXR (0.94, 95% CI: 0.92–0.97) and CAD4TB (0.92, 95% CI: 0.90–0.95). When matching the sensitivity of the radiologists, the specificities of the DL systems were significantly higher except for one. Using DL systems to read CXRs could reduce the number of Xpert MTB/RIF tests needed by 66% while maintaining sensitivity at 95% or better. Using a universal cutoff score resulted different performance in each site, highlighting the need to select scores based on the population screened. These DL systems should be considered by TB programs where human resources are constrained, and automated technology is available.

BackgroundBetter screening and testing approaches are needed to improve TB case finding, particularly in health facilities where many people with TB seek care but are not diagnosed using the existing approaches.ObjectiveWe aimed to evaluate the performance of various TB screening and testing approaches among hospital outpatients in a setting with a high prevalence of HIV/TB.MethodsWe screened outpatients at a large hospital in Cameroon using both chest X-ray and a symptom questionnaire including current cough, fever, night sweats and/or weight loss. Participants with a positive screen were tested for TB using smear microscopy, the Xpert MTB/RIF assay, and culture.ResultsAmong 2051 people screened, 1137 (55%) reported one or more TB symptom and 389 (19%) had an abnormal chest X-ray. In total, 1255 people (61%) had a positive screen and 31 of those screened (1.5%) had bacteriologically confirmed TB. To detect TB, screening with cough >2 weeks had a sensitivity of 61% (95% CI, 44–78%). Screening for a combination of cough >2 -weeks and/or abnormal chest X-ray had a sensitivity of 81% (95% CI, 67–95%) and specificity of 71% (95% CI, 69–73%), while screening for a combination of cough >2 weeks or any of 2 or more symptoms had a similar performance. Smear microscopy and Xpert MTB/RIF detected 32% (10/31) and 55% (17/31), respectively, of people who had bacteriologically-confirmed TB.ConclusionsScreening hospital outpatients for cough >2 weeks or for at least 2 of current cough, fever, night sweats or weight loss is a feasible strategy that had a high relative yield to detect bacteriologically-confirmed TB in this population. Clinical diagnosis of TB is still an important need, even where Xpert MTB/RIF testing is available.

In Cameroon, in 2019, tuberculosis (TB) treatment coverage was estimated at 53%, indicating that almost half of all people sick with TB were not diagnosed or linked to care. To inform strategies to improve access to TB services, we conducted an evaluation of the alignment between patient-initiated care-seeking behavior and spatial and institutional allocation of TB services. Data sources included the Cameroon Demographic and Health Survey (2018), the Health Facility List (2017), and routinely collected TB surveillance data. Data visualization was performed in Tableau and QGIS. The pathway analysis showed that only an estimated 9% of people attended a health facility providing TB services at initial care-seeking, with access varying from <3% to 16% across the ten regions of the country. While 72% of government and 56% of private hospitals (Level 2 facilities) provide TB services, most Cameroonians (87%) initially chose primary care (Level 1) or informal private sector sites (Level 0) without TB services. The gaps were greatest in regions with the highest prevalence of poverty, a significant determinant for TB. These results indicate that access may be improved by expanding TB services at both public and private facilities across the country, prioritizing regions with the greatest gaps.