Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening

Setting Tuberculosis (TB) screening programmes can be optimised by reducing the number of chest radiographs (CXRs) requiring interpretation by human experts. Objective To evaluate the performance of computerised detection software in triaging CXRs in a high-throughput digital mobile TB screening programme. Design A retrospective evaluation of the software was performed on a database of 38 961 postero-anterior CXRs from unique individuals seen between 2005 and 2010, 87 of whom were diagnosed with TB. The software generated a TB likelihood score for each CXR. This score was compared with a reference standard for notified active pulmonary TB using receiver operating characteristic (ROC) curve and localisation ROC (LROC) curve analyses. Results On ROC curve analysis, software specificity was 55.71% (95%CI 55.21-56.20) and negative predictive value was 99.98% (95%CI 99.95-99.99), at a sensitivity of 95%. The area under the ROC curve was 0.90 (95%CI 0.86-0.93). Results of the LROC curve analysis were similar. Conclusion The software could identify more than half of the normal images in a TB screening setting while maintaining high sensitivity, and may therefore be used for triage.