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11/20/2025 8:14:36 AM | Browse: 15 | Download: 40
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Received |
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2025-07-15 09:34 |
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Peer-Review Started |
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2025-07-15 09:34 |
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To Make the First Decision |
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Return for Revision |
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2025-08-14 08:53 |
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Revised |
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2025-08-26 00:46 |
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Second Decision |
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2025-10-14 02:46 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-10-14 07:09 |
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Articles in Press |
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2025-10-14 07:09 |
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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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2025-10-28 23:51 |
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Publish the Manuscript Online |
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2025-11-20 07:56 |
| ISSN |
1007-9327 (print) and 2219-2840 (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) 2024. 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 |
Surgery |
| Manuscript Type |
Review |
| Article Title |
Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Sami Akbulut, Zeynep Kucukakcali and Cemil Colak |
| Funding Agency and Grant Number |
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| Corresponding Author |
Sami Akbulut, FACS, MD, Professor, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com |
| Key Words |
Acute appendicitis; Complicated appendicitis; Artificial intelligence; Machine learning; Deep learning; Decision support systems; Explainable artificial intelligence; Predictive modeling; Diagnosis |
| Core Tip |
This comprehensive review explores the emerging role of artificial intelligence (AI), including machine learning and deep learning techniques, in diagnosing acute appendicitis (AAp). Despite advancements in imaging and clinical scoring, diagnosing AAp remains challenging, particularly in atypical cases. AI models such as random forests, support vector machines, and convolutional neural networks have demonstrated promising results in enhancing diagnostic accuracy and decision-making. In addition to aiding in the differential diagnosis of AAp from other causes of acute abdominal pain, AI approaches have also been applied to distinguish between complicated and uncomplicated appendicitis, thereby supporting risk stratification and guiding management strategies. The review discusses current evidence, potential benefits, and limitations of integrating AI-based decision support into clinical practice. These insights may pave the way for more precise, timely, and individualized management of AAp. |
| Publish Date |
2025-11-20 07:56 |
| Citation |
<p>Akbulut S, Kucukakcali Z, Colak C. Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications. <i>World J Gastroenterol</i> 2025; 31(43): 112000</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i43/112000.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i43.112000 |
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