Automated Classification of Otitis Media with OCT: Augmenting Pediatric Image Datasets with Gold-standard Animal Model Data

Overview

Journal Biomed Opt Express

Specialty Radiology

Date 2022 Jul 5

PMID 35781950

Authors

Guillermo L Monroy

Jungeun Won

Jindou Shi

Malcolm C Hill

Ryan G Porter

Michael A Novak

Wenzhou Hong

Pawjai Khampang

Joseph E Kerschner

Darold R Spillman Jr

Stephen A Boppart

Affiliations

Soon will be listed here.

Abstract

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.

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