2023 Fiscal Year Final Research Report
development of automatic diagnostic system of electromyographic discharges by audio information and artificial intelligence
| Project/Area Number |
20K07877
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 52020:Neurology-related
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| Research Institution | Tenri University (2023)
Tenri Health Care University (2021-2022)
Kanazawa Medical University (2020) |
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Principal Investigator |
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | 針筋電図 / 人工知能 / 音特徴量 |
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| Outline of Final Research Achievements |
Testing potentials of needle electromyography (needle EMG) obtained from patients with neuromuscular diseases were databased. The classified waveforms were divided into 2 second audio files. Method 1) Audio characteristics were obtained from each audio file. Machine learning methods was used to classify the six resting potentials. The accuracy was 90.4%. Method 2)The same database as the method 1 was used. The audio information was transformed into melspectrogram as image files. The images were divided into training and test data. The training data were then trained with convolutional neural networks (CNNs). Image augmentation was useful in that the accuracy was 100%.
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| Free Research Field |
臨床神経生理学
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| Academic Significance and Societal Importance of the Research Achievements |
針筋電図の放電判別は専門医以外には容易ではなく、正確な診断を妨げている。本研究により機械学習やディープラーニングを用いることで高精度に判別が可能であることが分かった。今後は実用化に向けて対象となる波形を増やしていく。また，教師データの不足が精度上昇のボトルネックになることが指摘されており，生成データをトレーニングデータへ流用できるか検討する必要がある。
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