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

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 limitation...

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Autores principales: Rustin G. Kashani, Marcel C. Młyńczak, David Zarabanda, Paola Solis-Pazmino, David M. Huland, Iram N. Ahmad, Surya P. Singh, Tulio A. Valdez
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Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/09c74aa4d1674e6b85eb51662d02251d
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spelling oai:doaj.org-article:09c74aa4d1674e6b85eb51662d02251d2021-12-02T17:23:51ZShortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach10.1038/s41598-021-91736-92045-2322https://doaj.org/article/09c74aa4d1674e6b85eb51662d02251d2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91736-9https://doaj.org/toc/2045-2322Abstract 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.Rustin G. KashaniMarcel C. MłyńczakDavid ZarabandaPaola Solis-PazminoDavid M. HulandIram N. AhmadSurya P. SinghTulio A. ValdezNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Rustin G. Kashani
Marcel C. Młyńczak
David Zarabanda
Paola Solis-Pazmino
David M. Huland
Iram N. Ahmad
Surya P. Singh
Tulio A. Valdez
Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
description 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.
format article
author Rustin G. Kashani
Marcel C. Młyńczak
David Zarabanda
Paola Solis-Pazmino
David M. Huland
Iram N. Ahmad
Surya P. Singh
Tulio A. Valdez
author_facet Rustin G. Kashani
Marcel C. Młyńczak
David Zarabanda
Paola Solis-Pazmino
David M. Huland
Iram N. Ahmad
Surya P. Singh
Tulio A. Valdez
author_sort Rustin G. Kashani
title Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
title_short Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
title_full Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
title_fullStr Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
title_full_unstemmed Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
title_sort shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/09c74aa4d1674e6b85eb51662d02251d
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