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.

No abstract

Background Chest X-ray (CXR) screening is a useful diagnostic tool to test individuals at high risk of tuberculosis (TB), yet image interpretation requires trained human readers who are in short supply in many high TB burden countries. Therefore, CXR interpretation by computer-aided detection software (CAD) may overcome some of these challenges, but evidence on its accuracy is still limited. We established a CXR library with images and metadata from individuals and risk groups  that underwent TB screening in a variety of countries to assess the diagnostic accuracy of three commercial CAD solutions through an individual participant meta-analysis. Methods and findings We collected digital CXRs and demographic and clinical data from 6 source studies involving a total of 2756 participants, 1753 (64%) of whom also had microbiological test information. All CXR images were analyzed with CAD4TB v6 (Delft Imaging), Lunit Insight CXR TB algorithm v4.9.0 (Lunit Inc.), and qXR v2 (Qure.ai) and re-read by an expert radiologist who was blinded to the initial CXR reading, the CAD scores, and participant information. While the performance of CAD varied across source studies, the pooled, meta-analyzed summary receiver operating characteristic (ROC) curves of the three products against a microbiological reference standard were similar, with area under the curves (AUCs) of 76.4 (95% CI 72.1-80.3) for CAD4TB, 83.3 (95% CI 78.4-87.2) for Lunit, and 76.4 (95% CI 72.1-80.3) for qXR. None of the CAD products, or the radiologists, met the targets for a triage test of 90% sensitivity and 70% specificity. At the same sensitivity of the expert radiologist (94.0%), all CAD had slightly lower point estimates for specificity (22.4% (95% CI 16.9-29.0) for CAD4TB, 34.6% (95% CI 25.3-45.1) for qXR, and 41.0% (95% CI 30.1-53.0) for Lunit compared to 45.6% for the expert radiologist). At the same specificity of 45.6%, all CAD products had lower point estimates for sensitivity but overlapping CIs with the sensitivity estimate of the radiologist. Conclusions We showed that, overall, three commercially available CAD products had a reasonable diagnostic accuracy for microbiologically confirmed pulmonary TB and may achieve a sensitivity and specificity that approximates those of experienced radiologists. While threshold setting and cost-effectiveness modelling are needed to inform the optimal implementation of CAD products as part of screening programs, the availability of CAD will assist in scaling up active case finding for TB and hence contribute to TB elimination in these settings.

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.

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.