Development of COVID-19 Imaging Tools with Artificial Intelligence

Division Of Basic Sciences - Nci

Investigators

Linked publications & trials

Abstract

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.