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Articles Published Processes
3/27/2025 2:55:00 AM | Browse: 20 | Download: 30
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Received |
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2025-02-05 02:39 |
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Peer-Review Started |
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2025-02-05 02:40 |
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2025-02-26 08:58 |
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2025-03-01 08:58 |
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Second Decision |
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2025-03-17 02:38 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-03-17 10:27 |
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Articles in Press |
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2025-03-17 10:27 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-03-26 01:05 |
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Publish the Manuscript Online |
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2025-03-27 02:55 |
| ISSN |
2689-7164 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Observational Study |
| Article Title |
Artificial intelligence model on images of functional dyspepsia
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Hiroshi Mihara, Sohachi Nanjo, Iori Motoo, Takayuki Ando, Haruka Fujinami and Ichiro Yasuda |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Hiroshi Mihara, Associate Professor, MD, PhD, Center for Medical Education, Sapporo Medical University, S1 W17, Chuo-ku, Sapporo 060-8556, Hokkaidō, Japan. m164.tym@gmail.com |
| Key Words |
Artificial Intelligence; Cloud-based; Duodenum; Functional dyspepsia |
| Core Tip |
This study reports a duodenal endoscopic artificial intelligence (AI) image model for detecting functional dyspepsia (FD). Endoscopic images of patients with FD typically lack labeled training data, as their alterations are imperceptible to human observers. In our previous study, we developed an AI model for irritable bowel syndrome and explored whether symptom presence or absence could serve as training labels. Our findings suggest that endoscopic duodenal images of FD patients can be distinguished with high accuracy from those of healthy individuals, stratified by HP infection status. Further investigations are warranted to assess AI's applicability in diagnosing other functional gastrointestinal disorders, as well as its potential for real-time FD image identification, investigation and treatment outcome prediction. |
| Publish Date |
2025-03-27 02:55 |
| Citation |
<p>Mihara H, Nanjo S, Motoo I, Ando T, Fujinami H, Yasuda I. Artificial intelligence model on images of functional dyspepsia. <i>Artif Intell Gastrointest Endosc</i> 2025; 6(1): 105674</p> |
| URL |
https://www.wjgnet.com/2689-7164/full/v6/i1/105674.htm |
| DOI |
https://dx.doi.org/10.37126/aige.v6.i1.105674 |
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