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public-private partnership; radiologist; radiomics; response; statistics; success; tool; treatment response; unpublished works; viral transmission; web site; working group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 部署了公共跨国 CT 数据集（迄今为止 650 个 CT），并进行数据共享和国际合作。 NIH 合作伙伴已让第三方部署了一个具有 CT 扫描拖放功能的网站。计算机行业合作伙伴联合开发了用于深度学习的算法和工具包，用于新冠人工智能分类的免费“免费软件”公共管道。通过公共/私人合作伙伴关系的联合学习，部署一个管道来促进深度学习，而没有隐私风险或限制，已被证明可用于共享模型的学术和研究用途。这允许共享人工智能模型权重，而无需实际数据本身从其私人控制源移动。 NIH 团队已验证来自成像行业合作伙伴的用于 COVID-19 特定分析和定量的预商业测试版软件。 免疫和炎症分子动力学与 CT AI 成像特征的相关性在 2020 财年过去几个月的临床试验中得到了验证。正在开发标准化工具，通过 CT 指标特征对 COVID-19 疾病进行统一量化：1.% COVID -19 受累，2. % 毛玻璃混浊，3. % 实变成分。假设这些指标与响应相关。与此同时，NIH 团队帮助根据大型中央注释源数据开发和验证公共和私人商业软件工具。 迫切需要标准化工具来测量和与临床结果相关，例如单克隆抗体或抗病毒治疗反应。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。尽管机器学习或基于放射组学的 CT 特征评估可能具有类似的功能，但对特定成像特征的依赖可能会导致数据点更少以及标准化和可重复性较差的工具。采用所提出的方法的深度学习和人工智能工具应该提供更具可重复性和标准化的模型，这些模型有更好的机会推广到非常广泛和异质的社区环境，其患病率和患者群体存在固有的变异性，并且人口动态动态变化。 开发的 CT AI 分类模型可检测 COVID-19 并将 COVID-10 与流感和其他非 COVID-19 诊断区分开来（Nature Communications 2020）。这种检测和区分/分类模型可能在北半球秋季流感季节有用。此外，该模型在护理点和紧急情况下可能有用，以便快速识别和隔离典型的 COVID-19 渗透。在一种使用方法中，在无症状患者被允许离开 CT 室之前，会出现护理点警报，以便随后进行快速放射科医生审查。 症状前 CT AI 混浊的时间动态与 COVID-19 症状前病毒动态相关。使用 CT AI 工具对无症状 CT 扫描进行量化。早期疾病患者的系列/序贯 CT 扫描（平均日期 > 40 天，每名患者 4 次扫描）以广义曲线统计显示，并与血清实验室（如 CRP、降钙素原、LDH、WBC 等）相关。这些轻度和早期疾病的广义曲线已被证明可以提供参考，从而能够预测偏离该曲线的情况作为不良结果或更高水平干预措施的风险因素。还开发了基于 CT AI 的肺部疾病自动化和标准化量化模型。 使用商业预发布测试版软件对 1000 个 CT 扫描进行的回顾性验证已经完成，并评估了观察者间的表现。与 Trans-NIH 工作组建立了合作伙伴关系，包括 NIAID IRF、NIBIB MIDCR、NCI、NCATS、NIDDK、N3C、RSNA、RICORD、ACR、AAPM 和 MITA。涉及 CT 等医学成像的 COVID-19 相关数据科学的集中通信和发现途径是一个共同的主题和目标。