Development of COVID-19 Imaging Tools with Artificial Intelligence

这将在NIH内部研究计划中进行试点，并最终将包括用于研究目的的机会，以上传DICOM CT图像，在胸部CT扫描中立即输出%可能性COVID。NIH CC和NCI是首批收集多国数据并开发基于COVID CT的免费公共人工智能解决方案的公司之一，供学术和商业开发人员使用。用于临床试验环境的统一和有效的成像生物标记物解决方案应该加快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。美国国立卫生研究院的模型可以检测新冠肺炎，并与H1N1流感、真菌或细菌性肺炎以及癌症、正常肺部和其他高性能实体区分开来。正在进行的工作将试图识别和标记CT病例以供放射科医生立即审查，从而标记和鼓励高度怀疑和/或无症状病例的隔离、聚合酶链式反应测试和接触者追踪。其他模型可能会根据早期的初始CT扫描，在最初的护理点预测以后对危重护理治疗的需求。CT反应量化的能力将使药物组合和治疗方法之间的关键跨平台比较成为可能，这是至关重要的，因为可能需要跨类别或药物加支持性治疗路径的联合治疗。研究还表明，症状前的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新冠肺炎的非人灵长类动物模型中得到了概括。先前与外部合作伙伴的工作表明，联合学习可以克服成像AI在不平衡源数据方面的缺点，并且应用特定的联合学习技术可以克服这一差距，从而表明不需要共享数据来从医学成像建立高质量的AI模型。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，可作为肺部受累的非侵入性生物标志物。与各种临床相关元数据的关联可能使在疫情爆发期间使用CT AI来识别CT生物标记物特征用于新冠肺炎的临床试验。这一努力将与许多校园努力交叉，包括临床前NIAID努力和新冠肺炎分类和表征图像处理的临床验证试验。正在收集和整理新冠肺炎的一个多国家数据集，以建立用于新冠肺炎分类和量化的公共模型，并已通过分析来自4个国家的数千例CT扫描证实，在CT扫描阳性的情况下，可能会同时存在无症状的病毒脱落。考虑到传染性高峰期可能是症状前期，CT作为一种有针对性的流行病学工具的有效性可能会在特定的有限情况下增加聚合酶链式反应和抗体检测。目标：促进验证用于建立用于量化的公共深度学习模型和用于表征新冠肺炎临床试验的标准反应标准度量的标准化工具。假设：CT成像数据聚合和人工智能将提供信息并加快新冠肺炎的临床和临床前研究。具体目标：开发、验证和翻译工具，通过深度学习方法对新冠肺炎病进行自动化和标准化的CT评估和量化，供临床试验期间使用。

This will be piloted in the NIH Intramural Research Program, and will eventually include opportunity for research purposes to uploading of DICOM CT images with an immediate output result of % likelihood COVID on chest CT scans. NIH CC and NCI have been 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 uniform and validated imaging biomarker solution for use for a clinical trial setting should 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 may predict the later need for critical care therapies based upon an initial CT scan early on, 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 likely necessity for combination therapies across classes or drug plus 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. 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 clinical trials in COVID-19. This effort will cross link 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. The validation of CT as a targeted epidemiological tool could potentially augment PCR and antibody testing in specific limited scenarios, given that peak infectivity may be pre-symptomatic. 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.