STTR Phase I: An Artificial Intelligence (AI)-based algorithm using nanosensor-based salivary analytics to predict clinical outcomes in symptomatic COVID-19 patients

Award ID: 2032579

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to provide U.S. hospitals with a tool to help accurately predict the expected severity of illness for COVID-19 infected patients at the time of initial diagnosis. A simple interface uses Artificial Intelligence-based predictive algorithms to help hospitals make informed and accurate decisions about which patients require specific care and treatment interventions. This enhanced process allows hospitals better and faster decision-making on patient care and treatments, redirecting hospital resources (including staff, hospital beds, ICU) for maximum effectiveness. In the longer term, the platform can be adapted to predict illnesses related to other infectious diseases, and also scaled for countries where availability of hospital infrastructure is limited. This Small Business Technology (STTR) Phase I project will develop a completely new category of medical diagnostic and prognostic tools via a novel approach that relies on analysis of a complex multi-variate signal, reflective of the patients entire salivary metabolome and proteome. Artificial intelligence tools will be used to see if signal clusters correlating with patient outcomes can be identified. This is a radical departure from traditional medical diagnostics which evaluate individual biomarkers for a clinical diagnosis. Such approaches are ill suited to the task of predicting future patient outcomes. The scope of this pilot phase work is the development of an effective algorithm and understanding algorithm efficacy and reliability in prediction and classification of outcomes for COVID-19 patients. The goals of the pilot project are to: (i) obtain COVID-19 patient bio-fluid samples, and (ii) develop machine learning techniques for an effective predictive algorithm. Multiple machine learning techniques and comparison strategies will be used for algorithm development and efficacy testing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Alastair Monk