Next Article in Journal
Image-Quality Assessment of Polyenergetic and Virtual Monoenergetic Reconstructions of Unenhanced CT Scans of the Head: Initial Experiences with the First Photon-Counting CT Approved for Clinical Use
Next Article in Special Issue
Deep Learning Based Detection Tool for Impacted Mandibular Third Molar Teeth
Previous Article in Journal
Renin-Angiotensin-Aldosterone System Inhibitors and Risk of Cancer: A Population-Based Cohort Study Using a Common Data Model
Previous Article in Special Issue
Artificial Intelligence Application in Assessment of Panoramic Radiographs
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 12
Issue 2
10.3390/diagnostics12020264
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Ariya Chantaramanee
1,
Kazuharu Nakagawa
2,*,
Kanako Yoshimi
2,
Ayako Nakane
2,
Kohei Yamaguchi
2 and
Haruka Tohara
2
1
Department of Preventive Dentistry, Naresuan University, Phitsanulok 65000, Thailand
2
Department of Dysphagia Rehabilitation, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(2), 264; https://doi.org/10.3390/diagnostics12020264
Submission received: 24 November 2021 / Revised: 15 January 2022 / Accepted: 19 January 2022 / Published: 21 January 2022
(This article belongs to the Special Issue Artificial Intelligence in Oral Health)
Browse Figures
Review Reports Versions Notes

Abstract

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.
Keywords: artificial intelligence; ultrasonography; tongue; algorithm; dysphagia artificial intelligence; ultrasonography; tongue; algorithm; dysphagia

Share and Cite

MDPI and ACS Style

Chantaramanee, A.; Nakagawa, K.; Yoshimi, K.; Nakane, A.; Yamaguchi, K.; Tohara, H. Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm. Diagnostics 2022, 12, 264. https://doi.org/10.3390/diagnostics12020264

AMA Style

Chantaramanee A, Nakagawa K, Yoshimi K, Nakane A, Yamaguchi K, Tohara H. Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm. Diagnostics. 2022; 12(2):264. https://doi.org/10.3390/diagnostics12020264

Chicago/Turabian Style

Chantaramanee, Ariya, Kazuharu Nakagawa, Kanako Yoshimi, Ayako Nakane, Kohei Yamaguchi, and Haruka Tohara. 2022. "Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm" Diagnostics 12, no. 2: 264. https://doi.org/10.3390/diagnostics12020264

APA Style

Chantaramanee, A., Nakagawa, K., Yoshimi, K., Nakane, A., Yamaguchi, K., & Tohara, H. (2022). Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm. Diagnostics, 12(2), 264. https://doi.org/10.3390/diagnostics12020264

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop