Development of COVID-19 Imaging Tools with Artificial Intelligence

点击翻译按钮获取中文摘要

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.