Artificial Intelligence in COVID-19, Procedural Medicine and Cancer

人工智能在 COVID-19、程序医学和癌症中的应用

• 批准号: 10920178
• 负责人: Bradford Wood
• 金额: --
• 依托单位: CLINICAL CENTER
• 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/10920178
• 关键词: 3-Dimensional; 3D Print; Algorithms; Angiography; Antibodies; Artificial Intelligence; Atelectasis; Bacterial Pneumonia; Biological Markers; COVID-19; COVID-19 detection; COVID-19 pandemic; COVID-19 patient; Cellular Phone; Classification; Clinical; Clinical Trials; Coagulation Process; Collaborations; Communicable Diseases; Communication; Computer software; Critical Care; Custom; Data; Data Science; Data Set; Detection; Development; Devices; Disease; Disease Outbreaks; Disease Outcome; Epidemiology; Event; Future; Goals; Hearing Tests; Home; Image; Industry; Infection; Institution; Institutional Review Boards; Interventional radiology; Investigation; Knowledge; Language; Learning; Link; Lung; Lung diseases; Malignant Neoplasms; Masks; Measures; Medical; Medical Imaging; Medical Research; Medicine; Methodology; Methods; Modality; Modeling; National Center for Advancing Translational Sciences; National Institute of Allergy and Infectious Disease; National Institute of Biomedical Imaging and Bioengineering; Nature; Oxygen; Oxygen saturation measurement; Pathway interactions; Patients; Pattern; Performance; Phenotype; Postoperative Period; Postpartum Period; Pre-Clinical Model; Prevention; Privacy; Privatization; Publishing; Reporting; Research Personnel; Resources; Running; Signal Transduction; Social Distance; Source; Standardization; Starvation; Suggestion; Techniques; Testing; Text; The Cancer Imaging Archive; Thoracic Radiography; Training; Triage; United States National Institutes of Health; Vasculitis; Ventilator; Voice; Weight; X-Ray Computed Tomography; cancer invasiveness; chest computed tomography; clinical imaging; coronavirus disease; data sharing; deep learning; deep learning model; design; disease classification; drug discovery; federated learning; flu; forging; fungal pneumonia; health care settings; image guided therapy; image processing; in vivo; influenza pneumonia; large datasets; migration; minimally invasive; multidisciplinary; multimodality; novel; outcome prediction; pandemic disease; point of care; post-COVID-19; public-private partnership; response; skills; smartphone application; smartphone based device; social media; tool; transfer learning; wearable device; working group

A multidisciplinary multi-institute, public-private partnership tackled the goal of developing and validating AI tools and standardized methodologies for clinical dynamics and novel classification tools for medical imaging, voice analysis, data sharing, and detection and prevention of dynamic diseases. A public pipeline for classification of COVID-19 (vs Flu) on chest CT was deployed. The NIH and extended team were among the first to gather multi-national data and develop freeware public AI solutions based on COVID CTs for academic, researcher, and commercial developer use. A uniform and standardized methodology for automatic classification of disease based on imaging, voice spectrograms, text input, or information from wearables could expedite the pathway towards drug discovery, infection outbreak and migration, and non-invasive quantification of disease such as vasculitis, post-operative clots or atelectasis. The NIH team developed and helped publicly post COVID-19 data and tools on TCIA and MIDRC, including the largest (summer 2020) chest CT dataset posted for the 1st year of the pandemic. NVIDIA and NIH co-developed AI models that detected COVID-19, differentiated from influenza, fungal, or bacterial pneumonias as well as other entities. AI models were able to predict the later need for critical care therapies based upon an initial CT scan early on, at the initial point of care. The public-private multinational partnership also used "federated learning" to train an AI model in 8 nations and 20 institutions that was able to predict subsequent oxygen needs based upon the initial point-of-care chest X-ray alone (Nature Medicine). This demonstrated methodology for data collaboration protects while maintaining privacy and allowing the data itself to remain at the home institution. Federated learning can in this way overcome shortcomings in unbalanced source data for AI, by sharing "model weights" instead of the actual data. This enabling technique overcome data sharing gaps, thus showing that the data does not need to be fully shared, in order to build quality AI models from medical imaging. The team also showed that CT AI can track disease in a predictable fashion in the pre-symptomatic, asymptomatic, and pauci-symptomatic patient, and that the general dynamic curve of disease has dynamic curve lab correlates may be predictive and recapitulate available preclinical models. Correlation with zip codes or cell phone towers could theoretically predict disease outbreak and migration patterns. CT image processing and deep learning models provide quantifiable metrics to serve as a noninvasive biomarkers for pulmonary involvement in COVID-19. A MICCAI AI data challenge in COVID-19 was organized around the data that the team curated. The NIH multi-national dataset (>3000 CTs /4 nations) showed that CT may be positive days before PCR. Thus, the suggestion that CT could function as a targeted epidemiological tool to perhaps augment PCR and antibody testing in specific limited scenarios or better define patterns of spread. Early signal for Omicron correlatives also led to development of a classification model purely from voice audiograms / spectrograms with high performance metrics, which was not true for Alpha and Delta. Partnerships with the Trans-NIH working group has been forged, including NIAID IRF, NIBIB MIDCR, NCI, NCATS, and N3C. Centralized communication and discovery pathways for COVID-19-related data science that involves medical imaging like CT or chest x-ray is a common theme and goal, and provides a fertile ground for advancement of data science with broad scope impact in cancer and in interventional radiology and well outside of infectious diseases. NIH participated in publishing and disseminating methods for handling COVID-19 in the angiography suite, details about post-partum COVID, designed and characterized a disposable isolation device ("full body mask") that reduces contamination in health care settings such as transport of COVID positive patients, validated in vivo a miniature 3D printable ventilator for resource-starved pandemic settings, and deployed a camera with custom software to identify social distancing distances with a standard webcam. A clinical trial for training AI models for Omicron detection from public social media audio data was IRB approved. Smartphone tools for instant anonynmization of imaging data were developed. A smartphone app for point-of-care deployment was created for running inference on clinical PACS 2D imaging or for cloud transmittal. Further collaborations with N3C, industry, Oxford and IRF NIAID were developed for assessment of AI tools with PPG, wearables, and smartphone apps.

多学科的多基金会，公私伙伴关系实现了开发和验证AI工具以及用于临床动力学和新型分类工具的标准化方法，用于医学成像，语音分析，数据共享，检测以及预防动态疾病。部署了COVID-19（vs Flu）分类的公共管道。 NIH和扩展团队是第一个收集跨国数据并开发基于COVID CTS的免费软件公共AI解决方案的团队之一，用于学术，研究人员和商业开发人员使用。一种基于成像，语音谱图，文本输入或可穿戴设备的信息自动分类的统一和标准化方法可以加快通往药物发现，感染爆发和迁移的途径，以及对诸如血管炎，后术后凝块或性疾病等疾病的无创量化。 NIH团队在TCIA和MIDRC上开发并帮助公开发布了COVID-19的数据和工具，其中包括最大的（2020年夏季）胸部CT数据集发布于大流行第一年。 NVIDIA和NIH共同开发的AI模型，该模型检测到COVID-19，与流感，真菌或细菌性肺炎以及其他实体区别开来。 AI模型能够根据初始护理时期的初始CT扫描来预测后来的重症监护疗法的需求。公私的跨国公司伙伴关系还使用“联邦学习”来培训8个国家和20个机构的AI模型，这些机构能够基于最初的护理胸部X射线（自然医学）来预测随后的氧气需求。这证明了数据协作的方法可以保护隐私，并允许数据本身留在家庭机构。联合学习可以通过共享“模型权重”而不是实际数据来克服AI不平衡源数据中的缺点。这种启用技术克服了数据共享差距，因此表明数据不需要完全共享，以便从医学成像中构建优质的AI模型。 该团队还表明，CT AI可以以可预测的方式以症状，无症状和症状性患者的身份跟踪疾病，并且疾病的一般动态曲线具有动态曲线实验室的相关性可能是预测的，并且可以识别可用的垂直模型。与邮政编码或手机塔的相关性可以从理论上预测疾病爆发和迁移模式。 CT图像处理和深度学习模型提供了可量化的指标，可作为COVID-19中肺部参与的非侵入性生物标志物。围绕团队策划的数据组织了COVID-19的MICCAI AI数据挑战。 NIH跨国数据集（> 3000 CTS /4个国家）表明，CT可能是PCR之前的正数。因此，在特定有限情况下或更好地定义扩散模式的情况下，CT可以充当有针对性的流行病学工具可以起到靶向流行病学工具的作用。 OMICRON相关性的早期信号还导致了纯粹来自具有高性能指标的语音听力图 /频谱图的分类模型，这对于Alpha和Delta而言并非如此。 与Trans-NIH工作组的合作伙伴关系已建立，包括Niaid IRF，Nibib MIDCR，NCI，NCAT和N3C。涉及CT或胸部X射线（例如CT或Chest X射线）的共同数据科学的集中式沟通和发现途径是一个共同的主题和目标，为数据科学的发展提供了肥沃的基础，这些数据科学在癌症和介入放射学方面具有广泛的影响以及在介入的放射学上以及在感染性疾病之外的良好。 NIH participated in publishing and disseminating methods for handling COVID-19 in the angiography suite, details about post-partum COVID, designed and characterized a disposable isolation device ("full body mask") that reduces contamination in health care settings such as transport of COVID positive patients, validated in vivo a miniature 3D printable ventilator for resource-starved pandemic settings, and deployed a camera with custom software通过标准网络摄像头确定社交距离距离。 IRB批准了一项培训来自公共社交媒体音频数据Omicron检测的AI模型的临床试验。开发了用于即时成像数据的智能手机工具。创建了用于保健点部署的智能手机应用程序，用于运行临床PACS 2D成像或云传输的推断。开发了与N3C，工业，牛津和IRF NIAID的进一步合作，以评估使用PPG，可穿戴设备和智能手机应用程序的AI工具。