Development of COVID-19 Imaging Tools with Artificial Intelligence

在NCI TCIA公共网站上公布了CT扫描的公共数据。AI深度学习模型是与多个行业合作伙伴一起制作的，以教育新冠肺炎的序列时间动力学。。构建了人工智能深度学习模型，并将其公开发布在合作伙伴的管道中用于研究目的，该模型可以在基于多国爆发训练数据的初始护理点CT扫描上自动分割新冠肺炎阴影并对新冠肺炎进行分类。模型输出是胸部CT扫描的%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。与MICCAI和国家儿童医学中心一起，还进行了一项数据挑战，供公众使用。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可以加快药物发现和早期验证或反应信号的进程。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。其他模型根据最初的CT扫描或在最初的护理点进行胸部X光检查来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能够进行关键的跨平台比较，这是至关重要的，因为在支持性治疗路径中需要跨药物类别的联合治疗。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。采用联合学习的胸部X光AI预测模型不久将发表在一本高影响力的期刊上。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生物标志物特征，以便在新冠肺炎的临床试验中进行标准化量化。这一努力与许多校园努力相交叉，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。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。CC/NCI团队成员还在猪身上部署了3D打印的微型呼吸机(现已商业化)，以及带有在线空气过滤的一次性隔离袋设备。CT人工智能模型被授权给业界。

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.