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Copyright © 2025, Nakabayashi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

This study aims to develop and evaluate a deep learning (DL) model for classifying tendon gliding sounds recorded using digital stethoscopes (Nexteto, ShareMedical, Japan, Nagoya). Specifically, we investigate whether differences in tendon excursion and biomechanics produce distinct acoustic signatures that can be identified through spectrogram analysis and machine learning (ML). Tendon disorders often present characteristic tactile and acoustic features, such as clicking or resistance during movement. In recent years, artificial intelligence (AI) and ML have achieved significant success in medical diagnostics, particularly through pattern recognition in medical imaging. Leveraging these advancements, we recorded tendon gliding sounds from the thumb and index finger in healthy volunteers and transformed these recordings into spectrograms for analysis. Although the sample size was small, we performed classification based on the frequency characteristics of the spectrograms using DL models, achieving high classification accuracy. These findings indicate that AI-based models can accurately distinguish between different tendon sounds and strongly suggest their potential as a non-invasive diagnostic tool for musculoskeletal disorders. This approach could offer a non-invasive diagnostic tool for detecting tendon disorders such as tenosynovitis or carpal tunnel syndrome, potentially aiding early diagnosis and treatment planning.

Details

Title
Quantitative Evaluation of Tendon Gliding Sounds and Their Classification Using Deep Learning Models
Author
Nakabayashi Daiji 1 ; Inui Atsuyuki 1 ; Mifune Yutaka 1 ; Yamaura Kohei 1 ; Kato Tatsuo 1 ; Furukawa Takahiro 1 ; Hayashi Shinya 1 ; Matsumoto, Tomoyuki 1 ; Matsushita Takehiko 1 ; Kuroda Ryosuke 1 

 Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, JPN 
University/institution
U.S. National Institutes of Health/National Library of Medicine
Publication year
2025
Publication date
2025
Publisher
Springer Nature B.V.
e-ISSN
21688184
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3204699485
Copyright
Copyright © 2025, Nakabayashi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.