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

Ariya Chantaramanee,Ayako Nakane,Haruka Tohara,Kohei Yamaguchi,Kazuharu Nakagawa,Kanako Yoshimi

doi:10.3390/diagnostics12020264

Ariya Chantaramanee, Ayako Nakane + Show 4 more

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https://doi.org/10.3390/diagnostics12020264

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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.

Highlights

Published: 21 January 2022Several recent studies have investigated the use of ultrasonography for evaluating the muscles of the head and neck, as it enables assessment of both muscle quality and quantity [1]
The findings indicate that tongue function was poorest in Group 1, but that there was no significant difference in tongue function between Groups 2 and 3
We used K-mean clustering algorithms on ultrasonographic images to categorize tongue characteristics based on muscle luminance and tongue thickness (TT) of healthy older individuals

Summary

Introduction

Several recent studies have investigated the use of ultrasonography for evaluating the muscles of the head and neck, as it enables assessment of both muscle quality and quantity [1]. In addition to qualitative characteristics such as tongue thickness (TT) and crosssectional area, ultrasonography can be used to assess qualitative characteristics of the tongue, such as the presence of intramuscular adipose tissue and muscle density. These qualitative parameters are represented in terms of echo intensity (EI) on grayscale ultrasonography images [5,6]. One recent study reported that lower EI values are associated with decreased tongue function and increased TT [7], while another identified decreased

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