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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
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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Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach

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