Development of COVID-19 and Cancer Tools with Artificial Intelligence

在NCI TCIA公共网站上发布了CT扫描的公共数据。人工智能深度学习模型是与多个行业合作伙伴一起制作的，以了解COVID-19的连续时间动态。人工智能深度学习模型被构建并公开发布用于研究目的，该模型基于多国训练数据，自动分割COVID-19不透明并对初始床旁CT扫描的COVID-19进行分类。NIH CC和NCI是最早收集多国数据并开发基于COVID CT的免费公共AI解决方案的机构之一，供学术和商业开发人员使用。用于临床试验环境的统一且经验证的成像生物标志物解决方案可以加快药物发现和早期验证或响应信号的途径。联合学习在Nature Medicine和JAMIA出版物的几个项目中与学术和行业合作伙伴进行了试点。NIH团队正在与商业和学术合作伙伴合作，评估深度学习工具在癌症中的应用。正在进行的工作将尝试在智能手机上部署语音模型，用于预先筛选设置。研究表明，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的非人灵长类动物模型中重现。先前与校外合作伙伴的工作已经证明，联邦学习可以克服成像AI的不平衡源数据的缺点，并且特定联邦学习技术的应用可以克服差距，从而表明数据不需要共享，以便从医学成像中构建高质量的AI模型。这项工作与许多校园工作交叉联系，包括临床前NIAID工作，NCI/CCR在AI资源中的工作，以及Bridge to AI中的校外合作伙伴。CC/NCI团队成员还在猪身上部署了一个3D打印的微型呼吸机（现已商业化），以及一个带有在线空气过滤的一次性隔离袋装置。CT AI模型被授权给工业。开发了具有高度影响力的AI模型，并获得许可，用于使用MRI和MRI-US融合活检检测、表征和评估前列腺癌。这些模型可能会产生广泛的影响。

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 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. 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 in Nature Medicine and JAMIA publications. The NIH team is working with commercial and academic partners to assess deep learning tools in cancer. 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. This effort cross links with numerous campus efforts, including preclinical NIAID efforts, NCI/CCR efforts within AI Resource, and extramural partners in Bridge to AI. 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. Highly impactful AI models were developed and licensed towards the detection, characterization, and assessment of prostate cancer using MRI and MRI-US fusion biopsy. These models may have broad scope impact.