[R21] Integrated computer-aided, point-of-care ultrasound for tuberculosis screening

项目摘要 结核分枝杆菌是导致传染病死亡的主要原因之一。 在全球范围内，全球病例侦破方面的差距持续存在。到2020年，预计只有580万人 确诊和报告了990万结核病患者。活动案例查找 通过对社区中的个人进行结核病筛查来发现这些未发现的病例的努力并不 在大多数资源有限的环境中是可行的，因为需要大量的痰筛查 微生物检测发现一个病例。一种快速、高度敏感的医疗点测试，可以 在现场进行高危个人筛查可以改善发现现役病例的机会 通过大幅减少测试所需的数量。尽管在这一领域取得了进展，但仍有 缺乏这样的测试，既能实现大规模可获得的护理点特征 用于分类或快速诊断测试的筛查和目标精确度分布。护理点 超声波(PICUS)设备低成本、便携、避免辐射暴露，并且不需要 经过培训的放射科工作人员，使他们能够以最小的资源需求适应大范围的需求。肺 已发现超声诊断成人肺炎的效率与改进后的水平相当 性能水平高于胸部X光检查，有望成为结核病分类测试。进一步适用于 基于人工智能的计算机辅助诊断算法可以进一步改进 通过自动化和标准化图像解释实现重复性，并有可能改进 随着不断发展的人工智能平台在不断增长的数据集上进行训练，性能也在不断提高。探索Pocus的用途 关于结核病筛查的计算机辅助设计，我们将在以下几个方面进行系统的评估 结核病家庭接触者涉及一系列早期到晚期感染表型。我们会 此外，还为基于Pocus的检测尝试了集成的计算机辅助检测算法 结合临床、暴露和患病率数据来探讨是否 这些变量可以提高结核病的特异性。然后，我们将使用以下工具评估性能配置文件 解构的基于特征的分析和深度学习算法，以告知潜力 将Pocus作为结核病分类测试的效用，使数百万人能够广泛发现活跃病例 未确诊的结核病病例。

PROJECT ABSTRACT Mycobacterium tuberculosis (TB, Mtb) is one of the leading causes of infectious disease mortality worldwide, with a persistent gap in global case detection. In 2020, only 5.8 million of the estimated 9.9 million individuals who became ill with TB were diagnosed and reported. Active case finding efforts to find these undetected cases by screening individuals in the community for TB is not feasible in most resource-limited settings due to the large number needed to screen with sputum microbiologic tests to detect one case. A rapid, highly sensitive point-of-care test that can be performed in the field to screen for high-risk individuals can improve access to active case finding by substantially reducing the number needed to test. Despite progress in the field, there remains a lack of such tests that achieve both the point-of-care characteristics accessible for large-scale screening and the target accuracy profiles for a triage or rapid diagnostic test. Point-of-care ultrasound (POCUS) devices are low-cost, portable, avoid radiation exposure, and do not require trained radiologic staff, making them amenable to wide scale with minimal resource needs. Lung ultrasound has been found to diagnose adult pneumonia at a comparable to improved performance level than chest-X-ray and holds promise as a TB triage test. Further application of artificial intelligence-based computer aided diagnosis algorithms can further improve reproducibility by automating and standardizing image interpretation, and potentially improve performance as evolving AI platforms train on growing datasets. To explore the utility of POCUS CAD for TB screening, we will conduct a systematic evaluation of POCUS for TB screening among TB household contacts across a spectrum of early to advanced infection phenotypes. We will additionally pilot an integrated computer-aided detection algorithm for POCUS-based detection of pulmonary TB with the integration of clinical, exposure, and prevalence data to explore whether these variables can increase specificity for TB. We will then evaluate performance profiles using both deconstructed feature-based analysis and deep-learning algorithms to inform the potential utility of POCUS as a TB triage test to enable widespread active case finding for the millions of undiagnosed TB cases.