Automated lung sound analysis to improve the clinical diagnosis of pulmonary tuberculosis in children

项目总结

PROJECT SUMMARY The majority of deaths from childhood tuberculosis (TB) are in children not initiated on treatment, highlighting the urgent need to prevent delays in diagnosis. However, the diagnosis of childhood TB is challenging, as sputum-based testing is invasive and has low yield in children, and chest X-ray (CXR) is not routinely available in high burden settings and requires trained readers. The World Health Organization (WHO) has therefore endorsed TB treatment decision algorithms to reduce delays in timely TB treatment initiation among children presenting to lower-level health facilities. However, TB treatment decision algorithms have uncertain accuracy and available data suggest they are likely to have poor specificity in the absence of CXR. Artificial-intelligence algorithms applied to lung sounds collected using a digital stethoscope (Lung AI) have the potential to enhance detection of lower respiratory tract disease in children being evaluated for TB. The overall objective of the proposed project is to evaluate whether Lung AI can improve the accuracy of TB treatment decision algorithms for childhood TB. We hypothesize that Lung AI can improve specificity while maintaining the high sensitivity of TB treatment decision algorithms. To assess this hypothesis, we will leverage 1) existing well-characterized cohorts to evaluate novel pediatric TB diagnostics; 2) expertise in digital stethoscopes and the development of Lung AI algorithms for TB; and 3) experience in assessing digital health tools in high TB burden settings. In Aim 1, we will examine the accuracy of new and current TB treatment decision algorithms using existing data collected from three ongoing childhood TB diagnostic cohorts in Uganda, South Africa, and the Gambia. At the same time, we will prospectively enroll children with TB symptoms in Uganda, perform a complete TB evaluation to classify TB status per NIH consensus definitions, and record lung sounds using a wireless digital stethoscope. In Aim 2, we will use these lung sounds to train Lung AI algorithms using deep learning models to identify lower airway abnormalities in children in reference to standardized lung sound definitions and radiology CXR reads. We will evaluate these models in an independent test set of children with TB symptoms and healthy children. In Aim 3, we will create a Lung AI model to detect microbiologically-confirmed or clinically-diagnosed TB in children, and compare its accuracy to 1) the best-performing TB treatment decision algorithm, and 2) a machine learning model that combines Lung AI and clinical variables, in the independent test set. Completion of these aims will determine the utility of TB treatment decision algorithms, while demonstrating the potential of a simple, affordable, digital health solution at the point-of-care to support the early diagnosis and treatment of TB and other respiratory diseases in children at lower-level health facilities.