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Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach

Authors :
Rustin G. Kashani
Marcel C. Młyńczak
David Zarabanda
Paola Solis-Pazmino
David M. Huland
Iram N. Ahmad
Surya P. Singh
Tulio A. Valdez
Source :
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

Abstract Otitis media, a common disease marked by the presence of fluid within the middle ear space, imparts a significant global health and economic burden. Identifying an effusion through the tympanic membrane is critical to diagnostic success but remains challenging due to the inherent limitations of visible light otoscopy and user interpretation. Here we describe a powerful diagnostic approach to otitis media utilizing advancements in otoscopy and machine learning. We developed an otoscope that visualizes middle ear structures and fluid in the shortwave infrared region, holding several advantages over traditional approaches. Images were captured in vivo and then processed by a novel machine learning based algorithm. The model predicts the presence of effusions with greater accuracy than current techniques, offering specificity and sensitivity over 90%. This platform has the potential to reduce costs and resources associated with otitis media, especially as improvements are made in shortwave imaging and machine learning.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.09c74aa4d1674e6b85eb51662d02251d
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-021-91736-9