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Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma
http://hdl.handle.net/10076/0002001036
http://hdl.handle.net/10076/000200103685c7779e-40f2-4c76-bd1f-b2ca1f90e251
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
2020DM0302.pdf (2.6 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||||||
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| 公開日 | 2024-11-06 | |||||||||
| タイトル | ||||||||||
| タイトル | Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma | |||||||||
| 言語 | en | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 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 |
池之山, 洋平
× 池之山, 洋平
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Background: It is known that an esophagus with multiple Lugol-voiding lesions (LVLs) after iodine staining is high risk for esophageal cancer; however, it is preferable to identify high-risk cases without staining because iodine causes discomfort and prolongs examination times. This study assessed the capability of an artificial intelligence (AI) system to predict multiple LVLs from images that had not been stained with iodine as well as patients at high risk for esophageal cancer. Methods: We constructed the AI system by preparing a training set of 6634 images from white-light and narrow-band imaging in 595 patients before they underwent endoscopic examination with iodine staining. Diagnostic performance was evaluated on an independent validation dataset (667 images from 72 patients) and compared with that of 10 experienced endoscopists. Results: The sensitivity, specificity, and accuracy of the AI system to predict multiple LVLs were 84.4 %, 70.0 %, and 76.4 %, respectively, compared with 46.9 %, 77.5 %, and 63.9 %, respectively, for the endoscopists. The AI system had significantly higher sensitivity than 9/10 experienced endoscopists. We also identified six endoscopic findings that were significantly more frequent in patients with multiple LVLs; however, the AI system had greater sensitivity than these findings for the prediction of multiple LVLs. Moreover, patients with AI-predicted multiple LVLs had significantly more cancers in the esophagus and head and neck than patients without predicted multiple LVLs. Conclusion: The AI system could predict multiple LVLs with high sensitivity from images without iodine staining. The system could enable endoscopists to apply iodine staining more judiciously. |
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| 言語 | en | |||||||||
| 内容記述 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 本文/Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan | |||||||||
| 内容記述 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 35p | |||||||||
| 書誌情報 |
発行日 2021-03-25 |
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| DOI | ||||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | 10.1055/a-1334-4053 | |||||||||
| フォーマット | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | application/pdf | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | VoR | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
| 出版者 | ||||||||||
| 出版者 | 三重大学 | |||||||||
| 出版者（ヨミ） | ||||||||||
| 値 | ミエダイガク | |||||||||
| 学位名 | ||||||||||
| 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 | 14101 | |||||||||
| 学位授与機関名 | 三重大学 | |||||||||
| 学位授与年月日 | ||||||||||
| 学位授与年月日 | 2021-03-25 | |||||||||
| 学位授与番号 | ||||||||||
| 学位授与番号 | 甲医学第2046号 | |||||||||
| ノート | ||||||||||
| 資源タイプ（三重大） | ||||||||||
| 値 | Doctoral Dissertation / 博士論文 | |||||||||
