Artificial Intelligence with Chest Imaging in COVID-19 and Isolation and Ventilator Devices for COVID-19

Clinical Center

Investigators

Linked publications, trials & patents

Abstract

A multidisciplinary multi-institute, public-private partnership tackled the goal of developing and validating a standardized methodology for clinical trial metrics and response criteria using CT Artificial Intelligence (AI). 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 quantification of lung disease could expedite the pathway towards drug discovery and early validation of response signals. 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 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 (Nature Communications, Aug 2020). Other AI models developed by NIH and NVIDIA appear to be able to predict the later need for critical care therapies based upon an initial CT scan early on, at the initial point of care (RSNA 2020). The NIH-NVIDIA 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 chest X-ray alone (submitted to Nature Medicine). This demonstrated methodology for data collaboration that protects privacy and allows the data to remain at the home institution. Federated learning can in this way overcome shortcomings in unbalanced source data for imaging AI, by sharing model weights instead of the actual data. This enabling technique overcame the gap, thus showing that the data does not need to be 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 recapitulated available preclinical models. CT image processing and deep learning models provide quantifiable metrics to serve as a noninvasive biomarker 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. Partnerships with the Trans-NIH working group has been forged, including NIAID IRF, NIBIB MIDCR, NCI, NCATS, NIDDK, N3C, RSNA, RICORD, ACR, AAPM, and MITA. Centralized communication and discovery pathways for COVID-19-related data science that involves medical imaging like CT is a common theme and goal. 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, validated in vivo a miniature ventilator for resource-starved pandemic settings, and deployed a camera with custom software to identify social distancing distances with a standard webcam.