Artificial Intelligence from Chest CT to Assess COVID-19 Clinical Trials

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In the short time during the pandemic, NIH, partners, and extended teams have deployed public multinational CT dataset via TCIA (650 CT's to date), with data sharing and international partnerships. NIH partners have had 3rd parties deploy a website with drag-and-drop functionality for CT scans. Computer industry partner has jointly developed algorithms and toolkits for deep learning for no-cost "freeware" public pipeline for COVID AI classification. Deployment of a pipeline to promote deep learning without he privacy risk or restrictions has been demonstrated for academic and research use with shared models, via federated learning via a public/private partnerships. This allows sharing of AI model weights, without the actual data itself moving around from its private controlled source. NIH team has validated pre-commercial beta software for COVID-19 specific analysis and quantification, from an imaging industry partner. Correlation of immune and inflammatory molecular dynamics with CT AI imaging profiles was validated in clinical trials during the past few months in FY 2020. Development is underway for standardized tools for uniform quantification of COVID-19 disease via CT characterization of metrics: 1. % COVID-19 involvement, 2. % ground glass opacities, 3. % consolidation components. These metrics are postulated to be correlates of response. In parallel, NIH teams helped develop and validate public and private commercial software tools from large central annotated source data. There is a critical need for standardized tools for measurement and correlation with clinical outcomes, such as monoclonal antibody or anti-viral therapy responses. Non-deep learning tools may measure simple density statistics, which is likely to be less specific. Although machine learning or radiomics-based CT feature assessment might perform a similar function, the reliance upon specific imaging features may result in fewer data points and a less standardized and reproducible tools. Deep learning and AI tools with the methodology proposed should provide more reproducible and standardized models, which have better chances for being generalized to a very broad and heterogeneous community setting, with inherent variabilities in prevalence and patient populations, and dynamically evolving population dynamics. The CT AI classification model developed detects COVID-19 and differentiates COVID-10 from influenza and other non-COVID-19 diagnoses (Nature Communications 2020). This detection and differentiation/classification model might be useful during the fall Northern hemisphere influenza season. In addition, the model may be useful at point of care and emergency settings in order to rapidly identify and isolate typical infiltrates of COVID-19. In one method of use, there would be point of care alarming for subsequent rapid radiologist review before an asymptomatic patient were allowed to leave the CT suite. Temporal dynamics of pre-symptomatic CT AI opacities correlated with COVID-19 pre-symptomatic viral dynamics. Asymptomatic CT scans were quantified with CT AI tools. Serial / sequential CT scans in patients with early disease (dating average of > 40 days and 4 scans per patient) were statistically displayed in generalized curves and correlated with serum labs such as CRP, pro-calcitonin, LDH, WBC, etc). These generalized curves in mild and early disease have been shown to provide a reference, enabling prediction of deviation from this curve as a risk factor for poor outcome or higher level interventions. Models have also been developed for automated and standardized quantification of lung disease based on CT AI. Retrospective validation of 1000 CT scans with a commercial pre-release beta software has been accomplished, with interobserver performance assessed. 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.