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Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images
https://repo.qst.go.jp/records/76802
https://repo.qst.go.jp/records/768026f93f1d4-a714-4af5-b841-4c5fd34a0188
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-09-17 | |||||
| タイトル | ||||||
| タイトル | Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images | |||||
| 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_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Shichijo, Satoki
× Shichijo, Satoki× Endo, Yuma× Aoyama, Kazuharu× Takeuchi, Yoshinori× Ozawa, Tsuyoshi× Takiyama, Hirotoshi× Matsuo, Keigo× Fujishiro, Mitsuhiro× Ishihara, Soichiro× Ishihara, Ryu× Tada, Tomohiro× Hirotoshi, Takiyama× Soichiro, Ishihara |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Background and aim: We recently reported the role of artificial intelligence in the diagnosis of Helicobacter pylori (H. pylori) gastritis on the basis of endoscopic images. However, that study included only H. pylori-positive and -negative patients, excluding patients after H. pylori-eradication. In this study, we constructed a convolutional neural network (CNN) and evaluated its ability to ascertain all H. pylori infection statuses. Methods: A deep CNN was pre-trained and fine-tuned on a dataset of 98,564 endoscopic images from 5236 patients (742 H. pylori-positive, 3649 -negative, and 845 -eradicated). A separate test data set (23,699 images from 847 patients; 70 positive, 493 negative, and 284 eradicated) was evaluated by the CNN. Results: The trained CNN outputs a continuous number between 0 and 1 as the probability index for H. pylori infection status per image (Pp, H. pylori-positive; Pn, negative; Pe, eradicated). The most probable (largest number) of the three infectious statuses was selected as the ‘CNN diagnosis’. Among 23,699 images, the CNN diagnosed 418 images as positive, 23,034 as negative, and 247 as eradicated.Because of the large number of H. pylori negative findings, the probability of H. pylori-negative was artificially re-defined as Pn 0.9, after which 80% (465/582) of negative diagnoses were accurate, 84% (147/174) eradicated, and 48% (44/91) positive. The time needed to diagnose 23,699 images was 261 seconds. Conclusion: We used a novel algorithm to construct a CNN for diagnosing H. pylori infection status on the basis of endoscopic images very quickly. Abbreviations: H. pylori: Helicobacter pylori; CNN: convolutional neural network; AI: artificial intelligence;EGD: esophagogastroduodenoscopies. |
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| 書誌情報 |
Scandinavian Journal of Gastroenterology 巻 54, 号 2, p. 158-163, 発行日 2019-02 |
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| 出版者 | ||||||
| 出版者 | Tayler & Francis | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0036-5521 | |||||
| PubMed番号 | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 30879352 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1080/00365521.2019.1577486 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.tandfonline.com/doi/full/10.1080/00365521.2019.1577486 | |||||
