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深層学習を用いた海馬硬化を伴う内側側頭葉てんかんの診断 : MRI研究
http://hdl.handle.net/10191/0002000823
http://hdl.handle.net/10191/0002000823018c82bb-5521-45c2-baf0-08187c654c24
| 名前 / ファイル | ライセンス | アクション |
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本文 (2.78MB)
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要旨 (556KB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||||||
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| 公開日 | 2023-03-07 | |||||||||
| タイトル | ||||||||||
| タイトル | Deep learning-based diagnosis of temporal lobe epilepsy associated with hippocampal sclerosis : An MRI study | |||||||||
| 言語 | en | |||||||||
| タイトル | ||||||||||
| タイトル | 深層学習を用いた海馬硬化を伴う内側側頭葉てんかんの診断 : MRI研究 | |||||||||
| 言語 | ja | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Mesial temporal lobe epilepsy | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Machine learning | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Convolutional neural network | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Temporal lobe epilepsy with hippocampal | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | sclerosis | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Fine-tuning | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Artificial intelligence | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||||
| 資源タイプ | doctoral thesis | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
| 著者 |
伊藤, 陽祐
× 伊藤, 陽祐
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Purpose: The currently available indicators—sensitivity and specificity of expert radiological evaluation of MRIs-to identify mesial temporal lobe epilepsy (MTLE) associated with hippocampal sclerosis (HS) are deficient, as they cannot be easily assessed. We developed and investigated the use of a novel convolutional neural network trained on preoperative MRIs to aid diagnosis of these conditions.Subjects and methods: We enrolled 141 individuals: 85 with clinically diagnosed mesial temporal lobe epilepsy (MTLE) and hippocampal sclerosis International League Against Epilepsy (HS ILAE) type 1 who had undergone anterior temporal lobe hippocampectomy were assigned to the MTLE-HS group, and 56 epilepsy clinic outpatients diagnosed as nonepileptic were assigned to the normal group. We fine-tuned a modified CNN (mCNN) to classify the fully connected layers of ImageNet-pretrained VGG16 network models into the MTLE-HS and control groups. MTLE-HS was diagnosed using MRI both by the fine-tuned mCNN and epilepsy specialists. Their performances were compared.Results: The fine-tuned mCNN achieved excellent diagnostic performance, including 91.1% [85%, 96%] mean sensitivity and 83.5% [75%, 91%] mean specificity. The area under the resulting receiver operating characteristic curve was 0.94 [0.90, 0.98] (DeLong's method). Expert interpretation of the same image data achieved a mean sensitivity of 73.1% [65%, 82%] and specificity of 66.3% [50%, 82%]. These confidence intervals were located entirely under the receiver operating characteristic curve of the fine-tuned mCNN.Conclusions: Deep learning-based diagnosis of MTLE-HS from preoperative MR images using our fine-tuned mCNN achieved a performance superior to the visual interpretation by epilepsy specialists. Our model could serve as a useful preoperative diagnostic tool for ascertaining hippocampal atrophy in patients with MTLE. | |||||||||
| 言語 | en | |||||||||
| 内容記述 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Epilepsy research. 2021, 178, 106815. | |||||||||
| 言語 | en | |||||||||
| DOI | ||||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1016/j.eplepsyres.2021.106815 | |||||||||
| 権利 | ||||||||||
| 言語 | en | |||||||||
| 権利情報 | © 2021 Elsevier B.V. All rights reserved. | |||||||||
| 学位名 | ||||||||||
| 言語 | ja | |||||||||
| 学位名 | 博士（医学） | |||||||||
| 学位授与機関 | ||||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||||
| 学位授与機関識別子 | 13101 | |||||||||
| 言語 | ja | |||||||||
| 学位授与機関名 | 新潟大学 | |||||||||
| 言語 | en | |||||||||
| 学位授与機関名 | Niigata University | |||||||||
| 学位授与年月日 | ||||||||||
| 学位授与年月日 | 2022-03-23 | |||||||||
| 学位授与番号 | ||||||||||
| 学位授与番号 | 甲第4967号 | |||||||||
| 学位記番号 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 新大院博(医)第1037号 | |||||||||
| 言語 | ja | |||||||||
