Artificial Intelligence from Chest CT to Assess COVID-19 Clinical Trials

利用胸部 CT 的人工智能评估 COVID-19 临床试验

• 批准号: 10262657
• 负责人: Bradford Wood
• 金额: --
• 依托单位: CLINICAL CENTER
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10262657
• 关键词: Algorithms; Antibodies; Antiviral Therapy; Artificial Intelligence; Bacterial Pneumonia; Biological Markers; COVID-19; Classification; Clinical; Clinical Research; Clinical Trials; Combined Modality Therapy; Communication; Computer software; Computers; Critical Care; Data; Data Aggregation; Data Science; Data Set; Detection; Development; Diagnosis; Digital Imaging and Communications in Medicine; Disease; Drops; Drug Combinations; Epidemiology; Failure; Glass; Goals; Image; Immune; Inflammatory; Influenza; Influenza A Virus, H1N1 Subtype; Institutes; Intervention; Learning; Lung; Lung diseases; Machine Learning; Malignant Neoplasms; Measurement; Measures; Medical Imaging; Metadata; Methodology; Methods; Modeling; Monoclonal Antibodies; National Institute of Allergy and Infectious Disease; National Institute of Biomedical Imaging and Bioengineering; National Institute of Diabetes and Digestive and Kidney Diseases; Nature; Online Systems; Outcome; Outcome Measure; Output; Pathway interactions; Patients; Peer Review; Performance; Pharmaceutical Preparations; Population Dynamics; Prevalence; Privacy; Privatization; Reproducibility; Research; Resource Allocation; Risk; Risk Factors; Scanning; Seasons; Serum; Signal Transduction; Software Tools; Source; Standardization; Supportive care; Symptoms; Techniques; Testing; The Cancer Imaging Archive; The Sun; Therapeutic; Time; United States National Institutes of Health; Vaccines; Validation; Viral; Weight; Work; X-Ray Computed Tomography; base; chest computed tomography; clinically relevant; community setting; coronavirus disease; cost; crosslink; data sharing; deep learning; density; disease phenotype; drug discovery; emergency settings; falls; fungal pneumonia; image processing; imaging biomarker; industry partner; influenza pneumonia; international partnership; large datasets; molecular dynamics; multidisciplinary; nonhuman primate; pandemic disease; patient population; point of care; pre-clinical; preclinical study; procalcitonin; public-private partnership; radiologist; radiomics; response; statistics; success; tool; treatment response; unpublished works; viral transmission; web site; working group

In the short time during the pandemic, NIH, partners, and extended teams have deployed public multinational CT dataset via TCIA (650 CT's to date), with data sharing and international partnerships. NIH partners have had 3rd parties deploy a website with drag-and-drop functionality for CT scans. Computer industry partner has jointly developed algorithms and toolkits for deep learning for no-cost "freeware" public pipeline for COVID AI classification. Deployment of a pipeline to promote deep learning without he privacy risk or restrictions has been demonstrated for academic and research use with shared models, via federated learning via a public/private partnerships. This allows sharing of AI model weights, without the actual data itself moving around from its private controlled source. NIH team has validated pre-commercial beta software for COVID-19 specific analysis and quantification, from an imaging industry partner. Correlation of immune and inflammatory molecular dynamics with CT AI imaging profiles was validated in clinical trials during the past few months in FY 2020. Development is underway for standardized tools for uniform quantification of COVID-19 disease via CT characterization of metrics: 1. % COVID-19 involvement, 2. % ground glass opacities, 3. % consolidation components. These metrics are postulated to be correlates of response. In parallel, NIH teams helped develop and validate public and private commercial software tools from large central annotated source data. There is a critical need for standardized tools for measurement and correlation with clinical outcomes, such as monoclonal antibody or anti-viral therapy responses. Non-deep learning tools may measure simple density statistics, which is likely to be less specific. Although machine learning or radiomics-based CT feature assessment might perform a similar function, the reliance upon specific imaging features may result in fewer data points and a less standardized and reproducible tools. Deep learning and AI tools with the methodology proposed should provide more reproducible and standardized models, which have better chances for being generalized to a very broad and heterogeneous community setting, with inherent variabilities in prevalence and patient populations, and dynamically evolving population dynamics. The CT AI classification model developed detects COVID-19 and differentiates COVID-10 from influenza and other non-COVID-19 diagnoses (Nature Communications 2020). This detection and differentiation/classification model might be useful during the fall Northern hemisphere influenza season. In addition, the model may be useful at point of care and emergency settings in order to rapidly identify and isolate typical infiltrates of COVID-19. In one method of use, there would be point of care alarming for subsequent rapid radiologist review before an asymptomatic patient were allowed to leave the CT suite. Temporal dynamics of pre-symptomatic CT AI opacities correlated with COVID-19 pre-symptomatic viral dynamics. Asymptomatic CT scans were quantified with CT AI tools. Serial / sequential CT scans in patients with early disease (dating average of > 40 days and 4 scans per patient) were statistically displayed in generalized curves and correlated with serum labs such as CRP, pro-calcitonin, LDH, WBC, etc). These generalized curves in mild and early disease have been shown to provide a reference, enabling prediction of deviation from this curve as a risk factor for poor outcome or higher level interventions. Models have also been developed for automated and standardized quantification of lung disease based on CT AI. Retrospective validation of 1000 CT scans with a commercial pre-release beta software has been accomplished, with interobserver performance assessed. Partnerships with the Trans-NIH working group has been forged, including NIAID IRF, NIBIB MIDCR, NCI, NCATS, NIDDK, N3C, RSNA, RICORD, ACR, AAPM, and MITA. Centralized communication and discovery pathways for COVID-19-related data science that involves medical imaging like CT is a common theme and goal.

在大流行期间的短时间内，NIH，合作伙伴和扩展团队通过TCIA（迄今为止650 CT）部署了公共跨国公司CT数据集，并具有数据共享和国际合作伙伴关系。 NIH Partners已有第三方部署了一个网站，该网站具有CT扫描的拖放功能。计算机行业合作伙伴已共同开发了用于深度学习的算法和工具包，用于无成本的“免费软件”公共管道进行COVID AI分类。通过公共/私人合作伙伴关系，通过联邦学习，已经证明了在没有HE隐私风险或限制的情况下促进深度学习或限制的管道的部署。这允许共享AI模型权重，而没有从其私有控制源移动的实际数据本身。 NIH团队已从成像行业合作伙伴中验证了商业前的Beta软件，用于COVID-19的特定分析和量化。 Correlation of immune and inflammatory molecular dynamics with CT AI imaging profiles was validated in clinical trials during the past few months in FY 2020. Development is underway for standardized tools for uniform quantification of COVID-19 disease via CT characterization of metrics: 1. % COVID-19 involvement, 2. % ground glass opacities, 3. % consolidation components.这些指标被认为是响应的相关性。同时，NIH团队从大型中央注释源数据中帮助开发和验证了公共和私人商业软件工具。 标准化工具非常需要测量和与临床结果（例如单克隆抗体或抗病毒疗法反应）相关。非深度学习工具可以测量简单的密度统计数据，这可能不太具体。尽管基于机器学习或基于放射线学的CT功能评估可能会执行相似的功能，但对特定成像功能的依赖可能会导致数据点较少，并且具有较低的标准化和可重现的工具。深度学习和AI工具具有所提出的方法，应提供更可重复的和标准化的模型，这些模型有更好的机会被推广到非常广泛和异类的社区环境，并具有固有的变异性和患者人群的固有变化，并动态发展了人口动态。 CT AI分类模型开发了COVID-19，并将Covid-10与流感和其他非旋转19诊断区分开（自然通信2020）。在秋季北半球流感季节，这种检测和分化/分类模型可能很有用。此外，该模型在护理点和紧急情况下可能很有用，以便快速识别和隔离Covid-19的典型浸润。在一种使用方法中，在允许无症状患者离开CT套件之前，将有一个护理点令人震惊。 症状前CT AI的时间动力学与COVID-19的症状前病毒动力学相关。用CT AI工具对无症状CT扫描进行定量。早期疾病患者的串行 /顺序CT扫描（平均约40天和4例患者的扫描）在统计上以广义曲线显示，并与血清实验室（例如CRP，pro-Calcitonin，LDH，WBC等）相关）。这些在温和疾病和早期疾病中的普遍曲线已被证明提供了参考，从而可以预测与此曲线的偏差为不良结果或更高水平干预措施的危险因素。还开发了基于CT AI的肺疾病的自动化和标准化的量化模型。 通过商业释放前Beta软件进行了1​​000 CT扫描的回顾性验证，并评估了观察者的性能。与Trans-NIH工作组建立了与Trans-NIH工作组建立的合作伙伴关系，包括Niaid IRF，Nibib Midcr，NCI，NCAT，NCATS，NIDDK，N3C，RSNA，RICORD，RICORD，ACR，ACR，AAPM，AAPM和MITA。与CT这样的医学成像涉及COVID-19相关数据科学的集中沟通和发现途径是一个共同的主题和目标。