Otitis Media Diagnosis and Treatment

PROJECT SUMMARY/ABSTRACT
Otitis media (OM) is the most common diagnosis in pediatric patients seen for illness in the United States (1,2),
affects 90% of all children (3,4) and is the most common indication for antimicrobial therapy and surgery (5) in young
children. Despite many attempts to improve diagnosis, treatment, and prevention, OM continues its highly
prevalent impact on children and substantial ongoing morbidity (1,3,6-30). OM continues as the most common cause
of hearing loss (HL) in children and leads to speech, educational and other developmental delays (31-37). OM
causes life-threatening complications (22,27) and is expensive, resulting in over $5 billion annually in U.S. health
care expenditures (3,38). 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 we will explore three specific aims. The first aim, part A, we will perform a comparative
assessment of middle ear (ME) pathology using pneumatic otoscopy (PO) and optical coherence tomography
(OCT) in pediatric patients that present to a primary care clinic with complaints of otalgia (ear pain) or OM, with
the hypothesis that OCT added to standard PO will improve diagnostic accuracy and reduce overall antibiotic
prescriptions. In part B of this aim, a comparative assessment of ME pathology using PO along with
audiology/tympanometry (TY) 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 PO and TY 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, we 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, we 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.

Grant Number: 7R01DC019412-05
NIH Institute/Center: NIH
Principal Investigator: Stephen Boppart