Development of COVID-19 Imaging Tools with Artificial Intelligence

利用人工智能开发 COVID-19 成像工具

• 批准号: 10487067
• 负责人: Bradford J Wood
• 金额: $ 23.23万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 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/10487067
• 关键词: 3D Print; Artificial Intelligence; Bacterial Pneumonia; Biological Markers; COVID-19; COVID-19 detection; COVID-19 pneumonia; Chest; Child; Classification; Clinical; Clinical Research; Clinical Trials; Combined Modality Therapy; Communicable Diseases; Communities; Contact Tracing; Critical Care; Data; Data Aggregation; Data Set; Development; Devices; Disease; Disease Outbreaks; Drug Combinations; Extramural Activities; Family suidae; Goals; Image; Imaging Device; Immunology; Industry; Influenza; Influenza A Virus, H1N1 Subtype; Journals; Laboratories; Learning; Lung; Malignant Neoplasms; Medical Imaging; Medical center; Metadata; Methodology; Modeling; National Institute of Allergy and Infectious Disease; Outcome; Outcome Measure; Output; Pathway interactions; Performance; Pharmaceutical Preparations; Publishing; Research; Resource Allocation; Signal Transduction; Source; Standardization; Supportive care; Techniques; Testing; The Cancer Imaging Archive; Therapeutic; Thoracic Radiography; Training; Translating; United States National Institutes of Health; Validation; Ventilator; Viral Pneumonia; Virus Shedding; Work; X-Ray Computed Tomography; air filtration; base; chest computed tomography; clinically relevant; coronavirus disease; crosslink; cytokine; deep learning; disease phenotype; drug discovery; fungal pneumonia; image processing; imaging biomarker; industry partner; influenza pneumonia; large datasets; member; model building; nonhuman primate; outcome prediction; pandemic disease; point of care; pre-clinical; preclinical study; predictive modeling; radiologist; response; success; tool; web site

Public data posting of CT scans on public NCI TCIA websites were made. AI deep learning models were made alongside of multiple industry partners, to educate on the serial temporal dynamics of COVID-19. . AI deep learning models were built and publicly posted on a partner's pipeline for research purposes that could automatically segment COVID-19 opacities and classify COVID-19 on an initial point of care CT scan, built on multi-national outbreak training data. Model output was % likelihood COVID on chest CT scans. NIH CC and NCI were among the first to gather multi-national data and develop freeware public AI solutions based on COVID CTs for both academic and commercial developer use. A data challenge was also done for public use alongside of MICCAI and Children's National Medical Center. A uniform and validated imaging biomarker solution for use for a clinical trial setting could expedite the pathway towards drug discovery and early validation or response signals. The NIH team is working with commercial and academic partners to assess quantification tools for COVID metrics. NIH models can detect COVID-19 and differentiate from H1N1 influenza, fungal, or bacterial pneumonias as well as cancer, normal lungs, and other entities with high performance. Ongoing work will attempt to identify and flag CT cases for immediate radiologist review, thus flagging and encouraging isolation, PCR testing, and contact tracing for high suspicion and or asymptomatic cases. Other models predict the later need for critical care therapies based upon an initial CT scan or chest x-ray at the initial point of care. The ability to standardize the quantification of CT responses would enable critical cross-platform comparisons among drug combinations and therapeutic approaches, which is vital, given the necessity for combination therapies across classes of drugs in supportive therapy pathways. It has also been shown that pre-symptomatic CT AI can track disease in a predictable fashion, and that this disease dynamic curve is recapitulated in a non-human primate model of COVID-19. Prior work with extramural partners has demonstrated that federated learning can overcome shortcomings in unbalanced source data for imaging AI, and that the application of a specific federated learning technique can overcome the gap, thus showing that the data does not need to be shared in order to build quality AI models from medical imaging. The chest X-ray AI predictive model using federated learning will soon be published in a high-impact journal. BACKGROUND / SIGNIFICANCE: CT image processing and deep learning models provide quantifiable metrics to serve as a noninvasive biomarker for pulmonary involvement. Correlation with a variety of clinically relevant metadata may enable the use of CT AI during outbreaks to identify CT biomarker features for standardized quantification in clinical trials for COVID-19. This effort cross links with numerous campus efforts, including preclinical NIAID efforts and clinical validation trials for image processing for classification and characterization in COVID-19. A multi-national dataset in COVID-19 is being collected and curated to build public models for COVID-19 classification and quantification and has verified that asymptomatic viral shedding may co-exist in the presence of a positive CT scan with analysis of thousands of CT scans from 4 nations. GOALS: Facilitate validation of a standardized tool for establishment of public deep learning models for quantification and standard response criteria metrics for characterization of COVID-19 clinical trials. HYPOTHESIS: CT imaging data aggregation and artificial intelligence will inform and expedite clinical and preclinical studies of COVID-19. SPECIFIC AIMS: Develop, validate, and translate tools for automated and standardized CT assessment and quantification of COVID-19 disease with deep learning methodologies for use during clinical trials. CC/NCI team members also deployed a 3D-printed miniature ventilator in swine (now commercialized) as well as a disposable isolation bag device with in-line air filtration. CT AI models were licensed to industry.

在公共NCI TCIA网站上发布了CT扫描的公共数据发布。 AI深度学习模型是与多个行业合作伙伴一起制定的，以教育Covid-19的串行时间动态。 。 AI深度学习模型是在合作伙伴的管道上建立和公开发布的，用于研究目的，该研究目的可以自动将COVID-19的不熟悉性细分，并根据基于跨国爆发训练数据的初始护理CT扫描进行对Covid-19进行分类。模型输出是胸部CT扫描上的类似可能性互联的。 NIH CC和NCI是最早收集跨国数据并开发基于COVID CTS的免费软件公共AI解决方案的人之一，用于学术和商业开发人员使用。与MICCAI和儿童国家医疗中心一起，还进行了数据挑战。用于临床试验设置的均匀且经过验证的成像生物标志物解决方案可以加快通往药物发现和早期验证或反应信号的途径。 NIH团队正在与商业和学术合作伙伴合作评估COVID指标的量化工具。 NIH模型可以检测到Covid-19，并与H1N1流感，真菌或细菌性肺炎以及癌症，正常肺和其他具有高性能的实体区分开。正在进行的工作将试图识别和标记CT案例，以进行立即放射科医生审查，从而标记和鼓励隔离，PCR测试以及接触tracing tracing，以提高怀疑和 /或无症状的病例。其他模型可以根据初始护理点的初始CT扫描或胸部X射线进行重症监护疗法的需求。标准化CT响应定量的能力将在药物组合和治疗方法之间进行关键的跨平台比较，这是至关重要的，鉴于有必要在支持治疗途径中进行跨类药物的组合疗法。还表明，症状前CT AI可以以可预测的方式跟踪疾病，并且这种疾病动态曲线在COVID-19的非人类灵长类动物模型中概括了。先前与校外伴侣的工作表明，联邦学习可以克服成像AI不平衡源数据中的缺点，并且特定联合学习技术的应用可以克服差距，因此表明该数据不需要共享数据以从医学成像中构建质量AI。使用联合学习的胸部X射线AI预测模型很快将在高影响力期刊上发布。背景 /意义：CT图像处理和深度学习模型提供了可量化的指标，可作为肺部参与的非侵入性生物标志物。与各种临床相关的元数据的相关性可以使在暴发期间使用CT AI，以在COVID-19的临床试验中识别CT生物标志物特征作为标准化定量的CT生物标志物特征。这项工作与众多校园努力联系起来，包括临床前的NIAID努力和临床验证试验，用于在Covid-19中进行分类和表征的图像处理。正在收集和策划COVID-19中的跨国数据集，以建立共同模型，以进行共同19的分类和量化，并证实，在存在正面CT扫描的情况下，无症状的病毒脱落可能会共存，并分析来自4个国家的数千次CT扫描。目标：促进用于建立公共深度学习模型的标准化工具，以定量和标准响应标准指标，以表征COVID-19的临床试验。假设：CT成像数据聚集和人工智能将为COVID-19的临床和临床前研究提供信息。具体目的：开发，验证和翻译用于自动化和标准化的CT评估和对Covid-19疾病的自动化和标准化的工具，并在临床试验期间使用深度学习方法进行使用。 CC/NCI团队成员还在猪（现已商业化）以及带有在线空气过滤的一次性隔离袋设备中部署了3D打印的微型呼吸机。 CT AI模型已获得行业许可。