Computer-aided interpretation of chest radiography to detect TB in rural South Africa

Background Computer-aided digital chest radiograph (CXR) interpretation can facilitate high-throughput screening for tuberculosis (TB), but its use in population-based screening has been limited. We applied an automated image interpretation algorithm, CAD4TBv5, prospectively in an HIV-endemic area. Methods Participants underwent CXR and, for those with symptoms or lung field abnormality, microbiological assessment of sputum collected at a mobile camp in rural South Africa. CAD4TBv5 scored each CXR on a 0-100 scale in the field. An expert radiologist, blinded to the CAD4TBv5 score and other data, assessed CXRs for 1) lung field abnormality and 2) findings diagnostic of active TB (R+). We estimated the performance of CAD4TBv5 for triaging (identifying lung field abnormality as a criteria for sputum examination) and diagnosis (detection of active TB as defined by microbiologic (M+) or radiologic (R+) gold standards). Findings For triaging, a CAD4TBv5 threshold of 25 identified abnormal lung fields with a sensitivity of 90.3% and specificity of 48.2%. For diagnosis, CAD4TBv5 had less agreement with the microbiological reference standard (M+) used to define definite TB (AUC 0.78) than with the radiological reference standard (R+) used to define probable TB (AUC 0.96). HIV-serostatus did not impact CAD4TB9s performance. Interpretation A low CAD4TBv5 threshold was required to achieve acceptable triaging sensitivity. Low specificity at this threshold led to high rates of sputum collection despite normal lung fields. CAD4TBv5 had difficulty identifying microbiologically-confirmed TB cases with subtle radiological features but had excellent agreement with the radiologist in identifying radiologically-defined TB cases. We conclude that computer-aided CXR interpretation can be useful in population-based screening in HIV-endemic settings, but threshold selection should be guided by setting-specific piloting and priorities. CXR interpretation algorithms require refinement for the identification of radiologically-subtle early TB.