I-Corps: AI-Guided Self Fitting Hearing Aid Platform

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project will benefit a broad range of medical device users, ranging from hearing aids to deep brain stimulators. The proposed innovation will enable efficient, self-directed tuning of medical devices using patient preference to find the settings that work best for each individual patient. The proposed innovation will improve patient outcomes and satisfaction, and thereby improve efficacy. Additionally, the innovation will reduce the need for patients to travel to expert clinicians, thereby improving access and reducing cost. This I-Corps project will first focus on exploring the application of this technology for creating a self-fitting hearing aid. This I-Corps project explores the market need for this self-directed tuning algorithm in medical devices. The core technology of the proposed innovation is an AI-guided self-fitting algorithm differentiates itself by efficiently tuning parameterized medical devices based upon user preference. By asking the patient to compare two settings, the innovation efficiently learns the patient's underlying preference, and finds their preferred setting in a small number of comparisons. The technology is currently being used in two pilot studies, where it is learning stimulation parameters for spinal cord stimulation and parkinson's disease. 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.