Sifrash Meseret Gelaw

The aim of this study was to independently evaluate the diagnostic accuracy of three artificial intelligence (AI)-based computer aided detection (CAD) systems for detecting pulmonary tuberculosis (TB) on global migrants screening chest x-ray (CXR) cases when compared against both microbiological and radiological reference standards (MRS and RadRS, respectively). Retrospective clinical data and CXR images were collected from the International Organization for Migration (IOM) pre-migration health assessment TB screening global database for US-bound migrants. A total of 2,812 participants were included in the dataset used for analysis against RadRS, of which 1,769 (62.9%) had accompanying microbiological test results and were included against MRS. All CXRs were interpreted by three CAD systems (CAD4TB v6, Lunit INSIGHT v4.9.0, and qXR v2) in offline setting, and re-interpreted by two expert radiologists in a blinded fashion. The performance was evaluated using receiver operating characteristics curve (ROC), estimates of sensitivity and specificity at different CAD thresholds against both microbiological and radiological reference standards (MRS and RadRS, respectively), and was compared with that of the expert radiologists. The area under the curve against MRS was highest for Lunit (0.85; 95% CI 0.83−0.87), followed by qXR (0.75; 95% CI 0.72−0.77) and then CAD4TB (0.71; 95% CI 0.68−0.73). At a set specificity of 70%, Lunit had the highest sensitivity (81.4%; 95% CI 77.9–84.6); at a set sensitivity of 90%, specificity was also highest for Lunit (54.5%; 95% CI 51.7–57.3). The CAD systems performed comparable to the sensitivity (98.3%), and except CAD4TB, to specificity (13.7%) of the expert radiologists. Similar trends were observed when using RadRS. Area under the curve against RadRS was highest for CAD4TB (0.87; 95% CI 0.86–0.89) and Lunit (0.87; 95% CI 0.85–0.88) followed by qXR (0.81; 95% CI 0.80–0.83). At a set specificity of 70%, CAD4TB had highest sensitivity (84.1%; 95% CI 82.3−85.8) followed by Lunit (80.9%; 95% CI 78.9−82.7); and at a set sensitivity of 90%, specificity was also highest for CAD4TB (54.6%; 95% CI 51.3−57.8). In conclusion, the study demonstrated that the three CAD systems had broadly similar diagnostic accuracy with regard to TB screening and comparable accuracy to an expert radiologist against MRS. Compared with different reference standards, Lunit performed better than both qXR and CAD4TB against MRS, and CAD4TB and Lunit better than qXR against RadRS. Moreover, the performance of the CADs can be impacted by characteristics of subgroup of population. The main limitation was that our study relied on retrospective data and MRS was not routinely done in individuals with a low suspicion of TB and a normal CXR. Our findings suggest that CAD systems could be a useful tool for TB screening programs in remote, high TB prevalent places where access to expert radiologists may be limited. However, further large-scale prospective studies are needed to address outstanding questions around the operational performance and technical requirements of the CAD systems.

The aim of this study was to independently evaluate the diagnostic accuracy of three artificial intelligence (AI)-based computer aided detection (CAD) systems for detecting pulmonary tuberculosis (TB) on global migrants screening chest x-ray (CXR) cases. Retrospective clinical data and CXR images were collected from the International Organization for Migration (IOM) pre-migration health assessment TB screening global database for US-bound migrants. A total of 2,812 participants were included in the dataset, of which 1,769 (62.9%) had accompanying microbiological test results. All CXRs were interpreted by three CAD systems (CAD4TB v6, Lunit INSIGHT v4.9.0, and qXR v2) offline and re-interpreted by two expert radiologists in a blinded fashion. The performance was evaluated using receiver operating characteristics curve (ROC), estimates of sensitivity and specificity at different CAD thresholds against both microbiological and radiological reference standards (MRS and RadRS, respectively). The area under the curve against MRS was highest for Lunit (0.85; 95% CI 0.83−0.87), followed by qXR (0.75; 95% CI 0.72−0.77) and then CAD4TB (0.71; 95% CI 0.68−0.73). At a set specificity of 70%, Lunit had the highest sensitivity (54.5%; 95% CI 51.7–57.3); at a set sensitivity of 90%, specificity was also highest for Lunit (81.4%; 95% CI 77.9–84.6). The CAD systems performed comparable to sensitivity (98.3%), and except CAD4TB, to specificity (13.7 %) of expert radiologist. Similar trends were observed when using RadRS. In conclusion, the study demonstrated that the three CAD systems had broadly similar diagnostic accuracy with regard to TB screening, and comparable accuracy to expert radiologist. Compared with different reference standards, Lunit performed better than both qXR and CAD4TB against MRS, and better than qXR against RadRS. Overall, these findings suggest that CAD systems could be a useful tool for TB screening programs in remote, high TB prevalent places where access to expert radiologists may be limited.

Background. Ethiopia is among highly tuberculosis affected countries. This might be related to low level of awareness on the disease in the population. The objective of the study was to determine the level of tuberculosis knowledge and socioeconomic factors associated with it.Methods. The 2011 Ethiopia health and demographic survey data were used. Overall tuberculosis knowledge score was computed to evaluate the outcome variable. Multivariable logistic regression was employed to identify independent socioeconomic factors associated with low tuberculosis knowledge.Results. The overall tuberculosis knowledge was low, 44.05% (95% CI: 42.05–46.24%) among women and 32.3% (95% CI: 30.34–34.32%) among men. Rural women (AOR = 1.22) and youth, no formal education (women: AOR = 3.28, men: AOR = 7.42), attending only primary education (women: AOR = 1.95, men: AOR = 3.49), lowest wealth quintiles (women: AOR = 1.4, Men: AOR = 1.28), unskilled female manual workers (AOR = 4.15), female agricultural employee (AOR = 2.28), and lack of access to media (women: AOR = 1.52, men: AOR = 1.71) are significantly associated with low tuberculosis knowledge.Conclusion. The level of tuberculosis knowledge among adults in Ethiopia is low and varied by socioeconomic groups. Tuberculosis control programs should consider appropriate strategies for tuberculosis education, promotion, communication, and social mobilization to address the rural women, youths, the poor, less educated people, and unskilled workers.