Computer-assisted diagnosis of ear pathologies by combining digital otoscopy with complementary data using machine learning

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ABSTRACT Diseases of the ear, particularly acute otitis media (AOM) and middle ear effusions, are the most commonly treated childhood pathologies. The financial burden of ear disease is estimated at more than $3.2 billion per year. Because ear diseases are common, a significant problem is over-diagnosis and over-treatment, due to two factors. First, the subjective nature of diagnosing ear disease - based on a brief glimpse of the eardrum with an otoscope - makes an accurate diagnosis difficult, even for experienced primary care, emergency medicine, or ear, nose, and throat (ENT) physicians. Second, with a growing shortage of primary care physicians in the US, more Advanced Practice Providers (Nurse Practitioners and Physician Assistants) serve as first-line clinicians in primary care and emergency settings but lack extensive training in otoscopy (i.e., clinical examination of the eardrum). Consequently, clinicians often err on the side of making a diagnosis of ear infection and prescribing oral antibiotics. Over 8 million unnecessary antibiotics are prescribed annually, contributing to the rise of antibiotic-resistant bacteria and creating the largest number of pediatric medication-related adverse events. Children with inaccurate ear diagnoses are often referred to ENTs for surgical placement of ear tubes for recurrent infections, and up to 70% of these cases are not indicated. Diagnosing ear pathologies still depends on clinician subjectivity, based on a brief glimpse of the eardrum. This diagnostic subjectivity creates a critical barrier to decreasing healthcare costs and reducing over-diagnosis and over-treatment of ear disease. Devices are needed to assist in a more accurate, consistent, and objective diagnosis of ear pathology. Our previous work laid the foundation to develop machine-learning approaches to provide an objective approach to ear diagnosis using digital otoscopy computer-assisted image analysis. This project will dramatically expand on our previous work with the overarching goal of developing new machine learning applications to analyze eardrum videos collected with a digital otoscope, which will be combined with tympanometry, demographic, and clinical data, to achieve diagnostic objectivity. The long-term goal is to improve clinicians’ diagnostic accuracy for ear diseases, using novel computer-assisted approaches. To accomplish these goals, we propose three Specific Aims: Specific Aim 1 will refine an objective computer-assisted image analysis (CAIA) software to differentiate multiple eardrum abnormalities. Specific Aim 2 will develop an otoscopy clinical decision support system by combining CAIA with additional data sources, including tympanometry, demographic, and clinical information. Specific Aim 3 will determine how the otoscopy clinical decision support system improves clinicians’ diagnostic performance.