Automated classification of otitis media with oct: augmenting pediatric image datasets with gold-standard animal model data

Otitis media (OM) is an extremely common disease that affects children worldwide. Optical coherence tomography (OCT) has emerged as a noninvasive diagnostic tool for OM, which can detect the presence and quantify the properties of middle ear fluid and biofilms. Here, the use of OCT data from the chinchilla, the gold-standard OM model for the human disease, is used to supplement a human image database to produce diagnostically relevant conclusions in a machine learning model. Statistical analysis shows the datatypes are compatible, with a blended-species model reaching similar to 95% accuracy and F1 score, maintaining performance while additional human data is collected. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement