NSF Award
2231883
The broader impact/commercial potential of this I-Corps project is the development of telehealth technology that shifts the care delivery model from in-person to virtual. Despite the wealth of recent medical artificial intelligence (AI) literature, few forms of AI translate from a theoretical context into clinical practice. The proposed technology enhances AI performance with novel, deep learning techniques, evaluates the usability and interpretability of an AI medical test in clinical practice, and measures whether use of the AI test alters antibiotic prescribing practices. This research may expand the knowledge around AI-based clinical decision support software used in clinical practice. Improved access to virtual healthcare can impact those who are most vulnerable, such as the underserved, those who lack transportation, those who have disabilities, or tjpse who have chronic illness. For example, if each of the 40 million strep throat visits were performed on telehealth instead of emergency rooms or urgent cares, health insurers could save $4-12 billion/year.<br/><br/>This I-Corps project is based on the development of a novel, deep learning algorithm that predicts strep throat using a smartphone video of the throat. It is currently feasible to detect strep throat from a smartphone photo by using pattern recognition of the tonsils and basic machine learning. However, the current technologies use high quality, single frame images that are impractical to acquire in a moving patient, or they use hardware not easily accessible to patients at home. A new approach is to use smartphone video for machine learning. Furthermore, there are visual changes unique to strep throat, such as redness or pus on the tonsils, that doctors can only identify with 55% accuracy. This technology applies pattern recognition of these visual changes to differentiate smartphone images of streptococcal pharyngitis (strep throat) from viral pharyngitis.<br/><br/>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.
