Chest X-ray evaluation using machine learning to support the early diagnosis of pulmonary TB

Abstract

<sec id="st1"><title>BACKGROUND</title>TB is a public health problem, and its diagnosis can be challenging. Among imaging methods, chest X-ray (CXR) is the leading choice for assessing pulmonary TB (PTB). Recent advancements in the field of artificial intelligence have stimulated studies evaluating the performance of machine learning (ML) for medical diagnosis. This study validated a new original Brazilian tool, the XmarTB, applied to CXR images to support the early diagnosis of PTB.</sec><sec id="st2"><title>METHODS</title>An ML model was trained on 3,800 normal images, 3,800 abnormal CXRs without PTB and 1,376 with PTB manifestations from the publicly available TBX11K database.</sec><sec id="st3"><title>RESULTS</title>The binary classification can distinguish between normal and abnormal CXR with a sensitivity of 99.4% and specificity of 99.4%. The XmarTB tool had a sensitivity of 98.1% and a specificity of 99.7% in detecting TB cases among CXRs with abnormal CXRs; sensitivity was 96.7% and specificity 98.7% in detecting TB cases among all samples.</sec><sec id="st4"><title>CONCLUSION</title>This diagnostic tool can accurately and automatically detect abnormal CXRs and satisfactorily differentiate PTB from other pulmonary diseases. This tool holds significant promise in aiding the proactive detection of TB cases, providing rapid and accurate support for early diagnosis.</sec>