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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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dc.contributor.authorViscaino, Michelle
dc.contributor.authorTalamilla, Matias
dc.contributor.authorMaass, Juan
dc.contributor.authorHenríquez, Pablo
dc.contributor.authorDélano, Paul
dc.contributor.authorAuat, Cecilia
dc.contributor.authorAuat, Fernando
dc.date.accessioned2023-12-15T18:31:43Z
dc.date.available2023-12-15T18:31:43Z
dc.date.issued2022
dc.description.abstractArtificial 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.
dc.description.versionVersión Publicada
dc.identifier.citationViscaino M, Talamilla M, Maass JC, Henríquez P, Délano PH, Auat Cheein C, Auat Cheein F. Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases. Diagnostics (Basel). 2022 Apr 7;12(4):917. doi: 10.3390/diagnostics12040917
dc.identifier.doihttps://doi.org/10.3390/diagnostics12040917
dc.identifier.urihttps://repositorio.udd.cl/handle/11447/8202
dc.language.isoen
dc.subjectArtificial intelligence
dc.subjectConvolutional neural network
dc.subjectDeep learning
dc.subjectMiddle and external ear
dc.subjectOtology
dc.titleColor Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourceDiagnostics
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscoverydd23ef58-7dc6-4a30-b4ac-ee0a59b1c68d

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