Detection of Otitis Media with Effusion Using In-Ear Microphones and Machine Learning

The diagnostic accuracy (ACC) of otitis media with effusion (OME) depends on a clinician's experience and evaluation tools. Various assessment technologies have been applied to support clinical diagnosis, such as digital otoscopy and tympanometry. However, several challenges and issues limit the capabilities and usability of these assessment technologies, including high costs and needing to rely on specialists' interpretations. In this work, we designed and validated OME detection using a machine learning (ML) model and in-ear microphones. Two off-the-shelf microphones were placed in the bilateral ear canals to record the voice when participants pronounced five 3-s sustained vowel sounds. Various signal processing and ML techniques were applied to the recordings, and the magnitude spectrograms of the vowel sound recording from in-ear microphones can distinguish ears with OME from healthy ears according to the differences in high-frequency response. Our results using in-ear microphones and ML algorithms had an ACC of 80.65% in detecting OME, similar to that of typical OME detection approaches. This work demonstrates the potential to provide healthcare practitioners with a simple, safe, and more reliable expert-level diagnostic tool.