Development of COVID-19 and Cancer 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。。AI深度学习模型是基于多国培训数据构建的，并公开发布在合作伙伴的管道中用于研究目的，该模型基于多国培训数据，在初始护理点CT扫描时自动分割新冠肺炎不透明区域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。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。联合学习与学术和行业合作伙伴在几个项目中进行了试点，其中包括一份《自然医学》出版物。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。正在进行的工作将尝试部署部署在智能手机上的语音模型，以进行预筛选设置。这表明，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重现。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。CT AI可作为新冠肺炎临床试验标准化量化的生物标志物。这一努力与许多校园努力相交叉，包括临床前NIAID在新冠肺炎中进行分类和表征的努力。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 automatically segment COVID-19 opacities and classify COVID-19 on an initial point of care CT scan, built on multi-national 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 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. Federated learning was piloted with academic and industry partners in several projects including a Nature Medicine publication. 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 deploy voice models deployed on smartphopnes for pre-screening settings. It was 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. CT AI might be a biomarker for standardized quantification in clinical trials for COVID-19. This effort cross links with numerous campus efforts, including preclinical NIAID efforts for classification and characterization in COVID-19. 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.