Coherent Optical Detection of Middle Ear Disease

Otitis media (OM) is the most common diagnosis in pediatric patients seen for illness in the United States, affects 90% of all children, and is the most common indication for antimicrobial therapy and surgery in young children. Despite many attempts to improve diagnosis, treatment, and prevention, OM continues its highly prevalent impact on children and substantial ongoing morbidity. OM continues as the most common cause of hearing loss in children and leads to speech, educational and other developmental delays. OM causes life-threatening complications and is expensive, resulting in over $5 billion annually in U.S. health care expenditures. Despite the prevalence and difficulties with OM, diagnostic accuracy to allow appropriate treatment is lacking, leading to misplaced resources in treating OM. This proposal builds on our central hypothesis that enhanced diagnostic tools, specifically, optical coherence tomography (OCT), will yield improved diagnosis and lead to reduced need for antibiotics to treat acute OM, reduced surgical interventions for chronic otitis media, and overall fewer complications and cost associated with this disease. In this proposal, the investigators will explore three specific aims. The first aim, part A, the investigators will perform a comparative assessment of middle ear pathology using otoscopy and OCT in pediatric patients that present to a primary care clinic with complaints of otalgia or OM, with the hypothesis that OCT added to standard otoscopy will improve diagnostic accuracy and reduce overall antibiotic prescriptions. In part B of this aim, a comparative assessment of middle ear pathology using otoscopy along with audiometry/tympanometry and OCT will be performed in pediatric patients that present to the pediatric otolaryngology clinic with a referral for chronic otitis media with effusion (OME), with the hypothesis that OCT added to standard otoscopy and audiometry/tympanometry will improve diagnostic accuracy and reduce overall need for surgery in patients with OME. In the second aim, using the OCT images captured in the previous aim, the investigators will develop image processing and machine learning algorithms for automated identification of effusions and biofilms in OCT image data to augment OM diagnosis for medical decision making. Finally, using the OCT images captured previously, along with our machine learning algorithms, the investigators will establish OCT B-mode and M-mode image-based features that predict the resolution or persistence of middle ear effusions over time. Collectively, this project will demonstrate how these advances in diagnostic tools and algorithms will improve diagnosis and provide added information for clinical decision making in the management of OM.