[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: 10647808
• 负责人: Yingda Linda Xie
• 金额: $ 18.97万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 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 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10647808
• 关键词: Address; Adult; Algorithms; Artificial Intelligence; Artificial Intelligence platform; Biological; Biological Assay; Biological Testing; 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; Machine Learning; Methodology; Methods; 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; Support System; Symptoms; System; Technology; Testing; Thoracic Radiography; Training; Triage; Tuberculosis; Tuberculosis diagnosis; Ultrasonography; case finding; cohort; computer aided detection; cost; deep learning algorithm; deep learning model; detection platform; digital; disability; evidence base; health care service; high risk population; improved; indexing; machine learning model; mortality; point of care; point of care testing; portability; 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（Mycobacterium tuberculosis，TB，Mtb）是导致感染性疾病死亡的主要原因之一 在全球范围内，在全球病例发现方面一直存在差距。到2020年，估计只有580万人 9.9有100万人被诊断和报告患有结核病。主动病例发现 通过对社区中的个人进行结核病筛查来发现这些未被发现的病例， 在大多数资源有限的环境中可行，因为需要大量痰液筛查 微生物学检测发现一例。一种快速、高灵敏度的床旁检测， 在现场进行高风险个体筛查可以改善主动病例发现的机会 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， 缺乏这样的测试，既实现了护理点的特点，可用于大规模 筛选和目标的准确性概况的分诊或快速诊断测试。Point-of-care 超声波（POCUS）设备成本低，便携式，避免辐射暴露，并且不需要 训练有素的放射工作人员，使他们能够以最小的资源需求进行大规模的。肺 已经发现超声诊断成人肺炎的速度与改进的 性能水平高于胸部X光检查，有望成为结核病分诊测试。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提高 通过自动化和标准化图像解释， 随着不断发展的人工智能平台在不断增长的数据集上训练，探索POCUS的效用 我们将对POCUS在结核病筛查中的应用进行系统评价， 结核病家庭接触者跨越早期到晚期感染表型的范围。我们将 此外，还试验了一种用于基于POSTO的检测的集成计算机辅助检测算法 结合临床、暴露和患病率数据，探讨是否 这些变量可以增加结核病的特异性。然后，我们将使用 解构的基于特征的分析和深度学习算法， POCUS作为结核病分诊测试的实用性，使数百万人能够广泛发现活跃病例， 未确诊的肺结核病例