SBIR Phase I: Development of a Machine Learning System to Identify Streptococcal Pharyngitis with a Smartphone Image

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project addresses the lack of instant, remote medical tests for telehealth. This project could develop an accurate machine learning-based predictive model for strep throat. The business model delivers an artificial intelligence (AI)-based clinical decision support system as a Software as a Service subscription to urgent care telehealth services. The total addressable market for all telehealth point of care tests (beyond strep throat) in urgent care and primary care is $10.4 billion. This solution impacts antibiotic overprescribing and economics of health services. Currently, 34% of children and 75% of adults with pharyngitis receive unnecessary antibiotics, and this is 10-21% worse with telehealth. A remote point of care prediction for strep throat can potentially reduce the $22 million/year costs in unnecessary antibiotics and reduce drivers for drug-resistant bacteria. When pharyngitis is treated on telehealth it saves patients up to 1-3 hours per clinical visit and saves health insurance companies up to $100-400 per visit, compared to an emergency room or urgent care facility. This Small Business Innovation Research (SBIR) Phase I project advances the field of machine learning by combining smartphone image analysis and deep learning. These strategies are applied to a novel use case in digital health as remote screening for clinical decision support. The technical challenge is the development of a predictive model to achieve sensitivity and specificity acceptable for clinical adoption, at a target of > 80% (similar to the rapid antigen strep test). The strategy to meet this challenge is to increase the size of the dataset and experiment with multiple prediction models until goal performance is achieved. The project will also include designing an authentication system that validates sufficient images as recorded by an untrained patient and creating an intuitive user interface that enables consistent recordings by patients. 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.