The diagnosis and treatment of otitis media (OM), a common childhood infection, is a significant burden on the healthcare system. Diagnosis relies on observer experience via otoscopy, although for non-specialists or inexperienced users, accurate diagnosis can be difficult. In past studies, optical coherence tomography (OCT) has been used to quantitatively characterize disease states of OM, although with the involvement of experts to interpret and correlate image-based indicators of infection with clinical information. In this paper, a flexible and comprehensive framework is presented that automatically extracts features from OCT images, classifies data, and presents clinically relevant results in a user-friendly platform suitable for point-of-care and primary care settings. This framework was used to test the discrimination between OCT images of normal controls, ears with biofilms, and ears with biofilms and middle ear fluid (effusion). Predicted future performance of this classification platform returned promising results (90%+ accuracy) in various initial tests. With integration into patient healthcare workflow, users of all levels of medical experience may be able to collect OCT data and accurately identify the presence of middle ear fluid and/or biofilms.
Keywords: Biomedical engineering; Imaging and sensing; Machine learning; Paediatric research; Translational research.