Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases

Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities u...
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Autor*in:	Michelle Viscaino [verfasserIn] Matias Talamilla [verfasserIn] Juan Cristóbal Maass [verfasserIn] Pablo Henríquez [verfasserIn] Paul H. Délano [verfasserIn] Cecilia Auat Cheein [verfasserIn] Fernando Auat Cheein [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	otology artificial intelligence middle and external ear deep learning convolutional neural network

Übergeordnetes Werk:	In: Diagnostics - MDPI AG, 2012, 12(2022), 4, p 917
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:4, p 917

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/diagnostics12040917

Katalog-ID:	DOAJ087162555

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language	English
source	In Diagnostics 12(2022), 4, p 917 volume:12 year:2022 number:4, p 917
sourceStr	In Diagnostics 12(2022), 4, p 917 volume:12 year:2022 number:4, p 917
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topic_facet	otology artificial intelligence middle and external ear deep learning convolutional neural network Medicine (General)
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container_title	Diagnostics
authorswithroles_txt_mv	Michelle Viscaino @@aut@@ Matias Talamilla @@aut@@ Juan Cristóbal Maass @@aut@@ Pablo Henríquez @@aut@@ Paul H. Délano @@aut@@ Cecilia Auat Cheein @@aut@@ Fernando Auat Cheein @@aut@@
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callnumber-first	R - Medicine
author	Michelle Viscaino
spellingShingle	Michelle Viscaino misc R5-920 misc otology misc artificial intelligence misc middle and external ear misc deep learning misc convolutional neural network misc Medicine (General) Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
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topic_title	R5-920 Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases otology artificial intelligence middle and external ear deep learning convolutional neural network
topic	misc R5-920 misc otology misc artificial intelligence misc middle and external ear misc deep learning misc convolutional neural network misc Medicine (General)
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title	Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
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title_auth	Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
abstract	Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.
abstractGer	Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.
abstract_unstemmed	Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.
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title_short	Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
url	https://doi.org/10.3390/diagnostics12040917 https://doaj.org/article/ae13ada94a18427594f6b57ff96b0e71 https://www.mdpi.com/2075-4418/12/4/917 https://doaj.org/toc/2075-4418
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author2	Matias Talamilla Juan Cristóbal Maass Pablo Henríquez Paul H. Délano Cecilia Auat Cheein Fernando Auat Cheein
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Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases

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