Open AccessAutomated classification of otitis media with OCT: augmenting pediatric image datasets with gold-standard animal model data
Author(s) -
Guillermo L. Monroy,
Jungeun Won,
Derek Shi,
Malcolm C. Hill,
Ryan G. Porter,
Michael Novák,
Wenzhou Hong,
Pawjai Khampang,
Joseph E. Kerschner,
Darold R. Spillman,
Stephen A. Boppart
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.453536
Subject(s) - gold standard (test) , optical coherence tomography , otitis , computer science , chinchilla , artificial intelligence , middle ear , pattern recognition (psychology) , medicine , radiology , surgery , anatomy
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 ∼95% accuracy and F1 score, maintaining performance while additional human data is collected.