Early diagnosis of amyotrophic lateral sclerosis based on fasciculations on muscle ultrasonography: A machine learning approach

• Material Type: 博士論文
• Title: Early diagnosis of amyotrophic lateral sclerosis based on fasciculations on muscle ultrasonography: A machine learning approach
• Author/Editor: Fukushima, Koji; Takamatsu, Naoko; Yamamoto, Yuki; Yamazaki, Hiroki; Yoshida, Takeshi; Osaki, Yusuke; Haji, Shotaro; Fujita, Koji; Sugie, Kazuma; Izumi, Yuishin
• Author Heading: Fukushima, Koji; Takamatsu, Naoko; Yamamoto, Yuki; Yamazaki, Hiroki; Yoshida, Takeshi; Osaki, Yusuke; Haji, Shotaro; Fujita, Koji; Sugie, Kazuma; Izumi, Yuishin
• Publication, Distribution, etc.: Elsevier B.V.
• Publication Date: 2022-08; 140
• Publication Date (W3CDTF): 2022-08
• Alternative Title: 筋超音波検査の線維束性収縮に基づく筋萎縮性側索硬化症の早期診断マーカー
• Degree grantor/type: 奈良県立医科大学
• Date Granted: 2024-03-14
• Date Granted (W3CDTF): 2024-03-14
• Dissertation Number: 甲第915号
• Degree Type: 博士（医学）
• Text Language Code: eng
• Subject Heading: Amyotrophic lateral sclerosis; Early diagnosis; Fasciculation; Machine learning; Muscle ultrasonography
• Target Audience: 一般
• Note (General): type:Thesis; "Objective: Although fasciculation on muscle ultrasonography (MUS) is useful in diagnosing amyotrophic lateral sclerosis (ALS), its applicability to early diagnosis remains unclear. We aimed to develop and validate diagnostic models especially beneficial to early-stage ALS via machine learning.Methods: We investigated 100 patients with ALS, including 50 with early-stage ALS within 9 months from onset, and 100 without ALS. Fifteen muscles were bilaterally observed for 10 s each and the presence of fasciculations was recorded. Hierarchical clustering and nominal logistic regression, neural network, or ensemble learning were applied to the training cohort comprising the early-stage ALS to develop MUS-based diagnostic models, and they were tested in the validation cohort comprising the laterstage ALS.Results: Fasciculations on MUS in the brainstem or thoracic region had high specificity but limited sensitivities and predictive profiles for diagnosis of ALS. A machine learning-based model comprising eight muscles in the four body regions had a high sensitivity (recall), specificity, and positive predictive value (precision) for both early- and later-stage ALS patients.Conclusions: We developed and validated MUS-fasciculation-based diagnostic models for early- and later-stage ALS.Significance: Fasciculation detected in relevant muscles on MUS can contribute to the diagnosis of ALS from the early stage."; 権利情報：@ 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.; identifier:Clinical Neurophysiology. 2022 Aug, vol.140, p.136-144; identifier:1388-2457; identifier:http://ginmu.naramed-u.ac.jp/dspace/handle/10564/4366; identifier:Clinical Neurophysiology, 140: 136-144
• Persistent ID (NDL): info:ndljp/pid/13722950; https://dl.ndl.go.jp/pid/13722950
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• Collection (particular): 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• Acquisition Basis: 博士論文（自動収集）
• Date Accepted (W3CDTF): 2024-07-05T22:07:01+09:00
• Format (IMT): application/pdf
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• Periodical Title (URI): http://hdl.handle.net/10564/4366
• Data Provider (Database): 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp