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

项目总结 儿童结核病(TB)的大多数死亡病例发生在未开始治疗的儿童身上，强调 迫切需要防止延误诊断。然而，儿童结核病的诊断是具有挑战性的，因为 以痰为基础的检测对儿童来说是侵入性的，成品率低，而且胸部X光(CXR)不是常规可用的 在高负担的环境下，需要训练有素的读者。因此，世界卫生组织(世卫组织) 批准结核病治疗决策算法，以减少儿童及时开始结核病治疗的延误 向较低级别的卫生机构提交报告。然而，结核病治疗决策算法具有不确定的准确性 而现有的数据表明，在没有CXR的情况下，它们的特异性很可能很差。人工智能 应用于使用数字听诊器(肺部AI)收集的肺音的算法有可能增强 正在评估结核病的儿童下呼吸道疾病的检测。《公约》的总体目标 提出的项目是评估肺人工智能是否可以提高结核病治疗决策算法的准确性 儿童结核病的治疗。我们假设，肺人工智能可以提高特异性，同时保持对 结核病治疗决策算法。为了评估这一假设，我们将利用1)现有的特征良好的 评估新的儿科结核病诊断方法的队列；2)数字听诊器的专业知识和 针对结核病的肺部人工智能算法；以及3)在评估高结核病负担环境中的数字健康工具方面的经验。在AIM 1，我们将使用现有数据检查新的和当前的结核病治疗决策算法的准确性 收集自乌干达、南非和冈比亚的三个正在进行的儿童结核病诊断队列。在 同时，我们将在乌干达招募有结核病症状的儿童，进行全面的结核病评估 根据NIH共识定义对结核病状态进行分类，并使用无线数字听诊器记录肺部声音。 在目标2中，我们将使用这些肺音来训练肺部人工智能算法，使用深度学习模型来识别较低的 CXR报道，参考标准化的肺音定义和放射学，儿童的呼吸道异常。 我们将在有结核病症状的儿童和健康儿童的独立测试集中对这些模型进行评估。在……里面 目标3，我们将创建一个肺部人工智能模型来检测经微生物确诊或临床诊断的儿童结核病， 并将其准确性与1)性能最佳的结核病治疗决策算法和2)机器学习进行比较 在独立测试集中，结合肺AI和临床变量的模型。完成这些目标将 确定结核病治疗决策算法的实用性，同时展示一种简单、 经济实惠的数字医疗保健解决方案，用于支持结核病和其他疾病的早期诊断和治疗 较低级别卫生机构的儿童呼吸系统疾病。

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.