2023 Fiscal Year Final Research Report

Development of new test methods and scoring criteria to detect chewing dysfunction with high accuracy using machine learning.

Research Project

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Project/Area Number	21K09977
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Multi-year Fund
Section	一般
Review Section	Basic Section 57050:Prosthodontics-related
Research Institution	Okayama University
Principal Investigator	Minakuchi Mami 岡山大学, 大学病院, 医員 (20634489)
Co-Investigator(Kenkyū-buntansha)	窪木拓男岡山大学, 医歯薬学域, 教授 (00225195) 水口一岡山大学, 大学病院, 講師 (30325097) 三木春奈岡山大学, 医歯薬学域, 助教 (60739902) 小山絵理岡山大学, 大学病院, 医員 (60779437)
Project Period (FY)	2021-04-01 – 2024-03-31
Keywords	口腔機能低下 / 低栄養 / 筋電図 / フレイル
Outline of Final Research Achievements	The purpose of this study was to clarify a method to easily and accurately detect a decline in masticatory function from electromyogram data obtained during meals. However, due to restrictions on contact with the elderly caused by the COVID-19 pandemic, it was difficult to conduct the originally planned clinical research on the elderly. Therefore, we focused on a method of analyzing and classifying electromyogram data obtained during various mandibular movements, which are oral functions similar to chewing movements, using machine learning, and examined the extent to which machine learning can discriminate electromyogram data obtained during movements similar to chewing movements. The results showed that the machine learning-based classification system had high accuracy in distinguishing between clenching and non-clenching movements using masseter muscle EMG.
Free Research Field	高齢者歯科学分野
Academic Significance and Societal Importance of the Research Achievements	要介護高齢者の増大に対して，要介護状態の発症を遅らせ，健康寿命を延伸することが強く望まれている。近年，フレイルが高齢者の自立喪失の有意なリスク因子であると報告された。フレイルサイクルの一端に口腔機能，特に咀嚼嚥下機能の低下による低栄養がある。そこで，口腔機能低下を早期に発見し，早期に口腔機能，栄養状態の改善を図ることができれば，高齢者の要介護状態への転落を遅延できると考えている。今回，口腔機能運動を咬筋筋活動から識別できたことは，将来的に口腔機能が維持された症例と低下している症例を筋電図検査から早期発見できる新たなスキームにつながる可能性を有している。