Otoscopy video screening with deep anomaly detection

Abstract

Ear infections are exceedingly common, yet challenging to diagnose correctly. The diagnosis requires a clinician (such as a physician, nurse practitioner, or physician assistant) to use an otoscope and inspect the eardrum (i.e. tympanic membrane). Once visualized the clinician must rely on clinical judgment to determine the presence of changes typically associated with an ear infection such as eardrum color and/or position. Research has however consistently demonstrated systemic failure among clinicians to correctly diagnose and manage ear infections. With recent advancements of pattern recognition techniques, including deep learning, there has been increasing interest in the opportunity to automate the diagnosis of ear infections. While there are some previous studies that successfully apply machine learning to classify ear drum photos, these methods were developed and evaluated in non-real world settings and used single, crisp, still-shot photos of the eardrum that would be labor-intensive to acquire in uncooperative pediatric patients. Contrary to previous works, we present a deep anomaly detection based method that flags otoscopy video sequences as normal or abnormal, achieving a promising first step towards automated analysis of otoscopy video for in-clinic or at-home screening.