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

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, STEPHEN A. BOPPART

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)3601-3614
Number of pages14
JournalBiomedical Optics Express
Volume13
Issue number6
DOIs
StatePublished - Jun 1 2022

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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