Development of COVID-19 and Cancer 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 automatically segment COVID-19 opacities and classify COVID-19 on an initial point of care CT scan, built on multi-national 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 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 including a Nature Medicine publication. 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 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. CT AI might be a biomarker for standardized quantification in clinical trials for COVID-19. This effort cross links with numerous campus efforts, including preclinical NIAID efforts for classification and characterization in COVID-19. 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.