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

[R21] 用于结核病筛查的集成计算机辅助床旁超声

• 批准号: 10511853
• 负责人: Yingda Linda Xie
• 金额: $ 22.31万
• 依托单位: RBHS-NEW JERSEY MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-16 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10511853
• 关键词: Address; Adult; Algorithms; Artificial Intelligence; Artificial Intelligence platform; Biological Assay; COVID-19 screening; Cessation of life; Characteristics; Clinical; Clinics and Hospitals; Communicable Diseases; Communities; Computer Assisted; Computer-Assisted Diagnosis; Computers; Data; Data Set; Decision Support Systems; Detection; Development; Devices; Diagnosis; Diagnostic tests; Early Intervention; Early treatment; Epidemiology; Equipment; Evaluation; Future; Goals; Health Services Accessibility; Health care facility; Household; Image; Image Analysis; Improve Access; Individual; Infection; Laboratories; Lung; Methodology; Methods; Microbiology; Morbidity - disease rate; Mycobacterium tuberculosis; Outcome; Parents; Performance; Phenotype; Pneumonia; Population; Prevalence; Protocols documentation; Pulmonary Tuberculosis; Radiation exposure; Radiology Specialty; Rapid diagnostics; Reporting; Reproducibility; Research; Resource-limited setting; Resources; Respiratory Tract Infections; Risk; Specificity; Sputum; Staging; Standardization; Supervision; Support System; Symptoms; System; Technology; Testing; Thoracic Radiography; Training; Triage; Tuberculosis; Tuberculosis diagnosis; Ultrasonography; base; case finding; cohort; computer aided detection; cost; deep learning algorithm; deep learning model; detection platform; digital; disability; evidence base; health care service; high risk; improved; indexing; machine learning model; mortality; point of care; point of care testing; portability; protocol development; scale up; screening; tool; transmission process; ultrasound

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.

项目摘要 结核分枝杆菌（TB，MTB）是传染病死亡率的主要原因之一 在全球范围内，在全球案例检测中存在持续的差距。在2020年，只有580万 诊断和报告了990万患病的人。活跃的案例查找 通过筛查社区中的结核病个人而不是 由于用痰筛选所需的大量数量，在大多数资源有限的设置中可行 微生物测试以检测一个情况。可以是快速，高度敏感的护理测试 在现场进行筛选高风险个体可以改善对活动案例查找的访问 通过大大减少测试所需的数量。尽管在现场进展，但仍有 缺乏这样的测试，这些测试既可以实现大规模的护理特征 筛选和目标精度分类或快速诊断测试。护理点 超声（POCUS）设备是低成本，便携式，避免辐射暴露，不需要 受过训练的放射线员，使他们可以在广泛的规模上满足资源需求。肺 已经发现超声可以诊断成人肺炎，可与 性能水平比Chest-X射线，并保持有望作为结核分诊测试。进一步应用 基于人工智能的计算机辅助诊断算法可以进一步改善 通过自动化和标准化图像解释并有可能改进来重现性 作为不断发展的AI平台的性能，可以在增长数据集上进行训练。探索pocus的效用 用于结核病筛查的CAD，我们将对POCUS进行系统评估，以进行结核病筛查 结核病家庭接触跨越早期感染表型。我们将 另外，用于基于POCUS检测的集成计算机辅助检测算法 肺结核与临床，暴露和患病率数据的整合 这些变量可以提高结核病的特异性。然后，我们将使用 基于特征的分析和深度学习算法都可以告知潜力 Pocus作为结核分型测试的实用程序，以实现数百万 未诊断的结核病病例。