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Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm

The precise correlations among tongue function and characteristics remain unknown, and no previous studies have attempted machine learning-based classification of tongue ultrasonography findings. This cross-sectional observational study aimed to investigate relationships among tongue characteristics...

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Autores principales: Chantaramanee, Ariya, Nakagawa, Kazuharu, Yoshimi, Kanako, Nakane, Ayako, Yamaguchi, Kohei, Tohara, Haruka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870935/
https://www.ncbi.nlm.nih.gov/pubmed/35204355
http://dx.doi.org/10.3390/diagnostics12020264
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author Chantaramanee, Ariya
Nakagawa, Kazuharu
Yoshimi, Kanako
Nakane, Ayako
Yamaguchi, Kohei
Tohara, Haruka
author_facet Chantaramanee, Ariya
Nakagawa, Kazuharu
Yoshimi, Kanako
Nakane, Ayako
Yamaguchi, Kohei
Tohara, Haruka
author_sort Chantaramanee, Ariya
collection PubMed
description The precise correlations among tongue function and characteristics remain unknown, and no previous studies have attempted machine learning-based classification of tongue ultrasonography findings. This cross-sectional observational study aimed to investigate relationships among tongue characteristics and function by classifying ultrasound images of the tongue using a K-means clustering algorithm. During 2017–2018, 236 healthy older participants (mean age 70.8 ± 5.4 years) were enrolled. The optimal number of clusters determined by the elbow method was 3. After analysis of tongue thickness and echo intensity plots, tongues were classified into three groups. One-way ANOVA was used to compare tongue function, tongue pressure, and oral diadochokinesis for /ta/ and /ka/ in each group. There were significant differences in all tongue functions among the three groups. The worst function was observed in patients with the lowest values for tongue thickness and echo intensity (tongue pressure [P = 0.023], /ta/ [P = 0.007], and /ka/ [P = 0.038]). Our results indicate that ultrasonographic classification of tongue characteristics using K-means clustering may aid clinicians in selecting the appropriate treatment strategy. Indeed, ultrasonography is advantageous in that it provides real-time imaging that is non-invasive, which can improve patient follow-up both in the clinic and at home.
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spelling pubmed-88709352022-02-25 Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm Chantaramanee, Ariya Nakagawa, Kazuharu Yoshimi, Kanako Nakane, Ayako Yamaguchi, Kohei Tohara, Haruka Diagnostics (Basel) Article The precise correlations among tongue function and characteristics remain unknown, and no previous studies have attempted machine learning-based classification of tongue ultrasonography findings. This cross-sectional observational study aimed to investigate relationships among tongue characteristics and function by classifying ultrasound images of the tongue using a K-means clustering algorithm. During 2017–2018, 236 healthy older participants (mean age 70.8 ± 5.4 years) were enrolled. The optimal number of clusters determined by the elbow method was 3. After analysis of tongue thickness and echo intensity plots, tongues were classified into three groups. One-way ANOVA was used to compare tongue function, tongue pressure, and oral diadochokinesis for /ta/ and /ka/ in each group. There were significant differences in all tongue functions among the three groups. The worst function was observed in patients with the lowest values for tongue thickness and echo intensity (tongue pressure [P = 0.023], /ta/ [P = 0.007], and /ka/ [P = 0.038]). Our results indicate that ultrasonographic classification of tongue characteristics using K-means clustering may aid clinicians in selecting the appropriate treatment strategy. Indeed, ultrasonography is advantageous in that it provides real-time imaging that is non-invasive, which can improve patient follow-up both in the clinic and at home. MDPI 2022-01-21 /pmc/articles/PMC8870935/ /pubmed/35204355 http://dx.doi.org/10.3390/diagnostics12020264 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
Chantaramanee, Ariya
Nakagawa, Kazuharu
Yoshimi, Kanako
Nakane, Ayako
Yamaguchi, Kohei
Tohara, Haruka
Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm
title Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm
title_full Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm
title_fullStr Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm
title_full_unstemmed Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm
title_short Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm
title_sort comparison of tongue characteristics classified according to ultrasonographic features using a k-means clustering algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870935/
https://www.ncbi.nlm.nih.gov/pubmed/35204355
http://dx.doi.org/10.3390/diagnostics12020264
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