並列タイトル等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
一般注記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
関連情報（DOI）10.1016/j.clinph.2022.06.005
連携機関・データベース国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
提供元機関・データベース奈良県立医科大学 : 奈良県立医科大学機関リポジトリ GINMU