Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Viscaino, Michelle, Talamilla, Matias, Maass, Juan Cristóbal, Henríquez, Pablo, Délano, Paul H., Auat Cheein, Cecilia, Auat Cheein, Fernando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9031192/
https://www.ncbi.nlm.nih.gov/pubmed/35453965
http://dx.doi.org/10.3390/diagnostics12040917
_version_ 1784692329626468352
author Viscaino, Michelle
Talamilla, Matias
Maass, Juan Cristóbal
Henríquez, Pablo
Délano, Paul H.
Auat Cheein, Cecilia
Auat Cheein, Fernando
author_facet Viscaino, Michelle
Talamilla, Matias
Maass, Juan Cristóbal
Henríquez, Pablo
Délano, Paul H.
Auat Cheein, Cecilia
Auat Cheein, Fernando
author_sort Viscaino, Michelle
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9031192
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-90311922022-04-23 Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases Viscaino, Michelle Talamilla, Matias Maass, Juan Cristóbal Henríquez, Pablo Délano, Paul H. Auat Cheein, Cecilia Auat Cheein, Fernando Diagnostics (Basel) Article 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. MDPI 2022-04-07 /pmc/articles/PMC9031192/ /pubmed/35453965 http://dx.doi.org/10.3390/diagnostics12040917 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Viscaino, Michelle
Talamilla, Matias
Maass, Juan Cristóbal
Henríquez, Pablo
Délano, Paul H.
Auat Cheein, Cecilia
Auat Cheein, Fernando
Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
title Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
title_full Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
title_fullStr Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
title_full_unstemmed Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
title_short Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
title_sort color dependence analysis in a cnn-based computer-aided diagnosis system for middle and external ear diseases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9031192/
https://www.ncbi.nlm.nih.gov/pubmed/35453965
http://dx.doi.org/10.3390/diagnostics12040917
work_keys_str_mv AT viscainomichelle colordependenceanalysisinacnnbasedcomputeraideddiagnosissystemformiddleandexternaleardiseases
AT talamillamatias colordependenceanalysisinacnnbasedcomputeraideddiagnosissystemformiddleandexternaleardiseases
AT maassjuancristobal colordependenceanalysisinacnnbasedcomputeraideddiagnosissystemformiddleandexternaleardiseases
AT henriquezpablo colordependenceanalysisinacnnbasedcomputeraideddiagnosissystemformiddleandexternaleardiseases
AT delanopaulh colordependenceanalysisinacnnbasedcomputeraideddiagnosissystemformiddleandexternaleardiseases
AT auatcheeincecilia colordependenceanalysisinacnnbasedcomputeraideddiagnosissystemformiddleandexternaleardiseases
AT auatcheeinfernando colordependenceanalysisinacnnbasedcomputeraideddiagnosissystemformiddleandexternaleardiseases