| Project/Area Number |
21K19621
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 58:Society medicine, nursing, and related fields
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| Research Institution | Iwate University |
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Principal Investigator |
Sasaki Makoto 岩手大学, 理工学部, 准教授 (80404119)
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| Co-Investigator(Kenkyū-buntansha) |
玉田 泰嗣 長崎大学, 病院(歯学系), 助教 (50633145)
土井 めぐみ 長崎大学, 病院(医学系), 技術職員 (50899044)
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| Project Period (FY) |
2021-07-09 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥6,370,000 (Direct Cost: ¥4,900,000、Indirect Cost: ¥1,470,000)
Fiscal Year 2022: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2021: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
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| Keywords | 嚥下障害 / 不顕性誤嚥 / マルチモーダル生体信号計測 / AI / 機械学習 / 摂食嚥下 |
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| Outline of Research at the Start |
不顕性誤嚥は，ムセや自覚症状がなく，静かに肺炎を発症・進行させるため，医療従事者や介護者であってもその兆候や変化を捉えることは難しく，嗄声や発熱等を認めた時点で既に重症化している場合が多い．そこで本研究では，睡眠時生体信号から不顕性誤嚥群を検出しうる，新しいAI手法の開発を目的とする．さらに，AIの判断基準を見える化し，臨床的考察を加えることで，そのメカニズム解明に挑戦する．
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| Outline of Final Research Achievements |
We investigated the possibility of detecting subclinical aspiration from multimodal biological signals during sleep and basic physiological data during wakefulness. Analysis of the awake data using artificial intelligence (AI) revealed that the movements of swallowing-related organs could be classified from surface electromyography signals of the hyoid muscles. In addition, we found that the risk of aspiration during sleep could be evaluated based on changes in breathing patterns before and after swallowing detected by the polyvinylidene fluoride film sensor. In analysis of the sleep data, we measured multimodal biological signals of a dysphagic person with subclinical aspiration and automatically detected saliva swallowing and respiratory changes before and after the swallowing. Furthermore, the feasibility of classifying subclinical aspiration groups by AI was verified, and basic knowledge for a new monitoring technique for dysphagia patients was obtained.
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| Academic Significance and Societal Importance of the Research Achievements |
不顕性誤嚥は，ムセや自覚症状がなく，静かに肺炎を発症・進行させるため，医療従事者や介護者であってもその兆候や変化を捉えることは難しい．本研究では，睡眠時マルチモーダル生体信号と覚醒時基礎データからの不顕性誤嚥患者の検出可能性について基礎的検討を行ったが，患者に共通する生体信号の特徴とその検出法をより詳細に解明できれば，不顕性誤嚥を理解，克服するうえでの新たな展開が期待できる．例えば，不顕性誤嚥の検出法や，誤嚥性肺炎発症の予測システムなどが考えられ，これらの実現に向けた第一歩として，本成果の学術的，社会的意義が認められる．
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