Improving Rural Access to Pediatric Healthcare with Accurate Telehealth Diagnosis of Acute Otitis Media using Smartphone Acoustic Reflectometry

PROJECT SUMMARY Acute otitis media (AOM) is a leading cause of pediatric visits each year, affecting 85% of children under the age of 3. There are 18 million annual doctor visits due to suspected AOM, which today are challenging to conduct over telehealth because diagnosis traditionally requires an in-person physical exam. This is particularly problematic for rural children who can’t easily access healthcare services leading to possible complications from under-treatment (hearing loss, speech delay, eardrum perforation). Current telehealth services lack technology to successfully conduct a middle ear fluid exam, the key finding of the physical exam for an AOM. Wavely Diagnostics is developing a smartphone-based tool for lay users that accurately detects middle ear fluid during a telehealth visit. Our technology uses acoustic reflectometry—an impedance-based technique that reflects sound chirps off the eardrum—paired with machine learning algorithms to determine middle ear fluid status. In this Direct to Phase II SBIR project, the specific aims are: 1) develop an in-field calibration process to drive compatibility across smartphones, 2) clinically demonstrate accuracy of middle ear fluid detection with device as compared to physician assessment, 3) develop a HIPAA-compliant Wavely Application Programing Interface (API) to integrate with a telemedicine platform, and 4) validate the clinical utility of Wavely Middle Ear Fluid Scanner for diagnosing AOM in a telemedicine setting. The expected outcome of this proposal is a Middle Ear Fluid Scanner for AOM detection that will be ready for widescale use by telehealth services supporting rural populations. Given the ubiquity of smartphones across all communities, recent rise in telehealth usage particularly in rural communities where significant investment is being made in broadband services, and the critical need for accurate remote diagnostics, our proposed system has the potential to significantly improve healthcare access for the 13.4 million children in rural communities.