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Articles Published Processes
7/22/2025 9:23:13 AM | Browse: 3 | Download: 20
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Received |
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2025-03-19 13:16 |
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Peer-Review Started |
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2025-03-19 13:16 |
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To Make the First Decision |
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Return for Revision |
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2025-04-16 10:40 |
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Revised |
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2025-04-26 18:07 |
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Second Decision |
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2025-06-18 02:39 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-06-18 10:44 |
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Articles in Press |
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2025-06-18 10:44 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2025-06-22 21:11 |
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Typeset the Manuscript |
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2025-07-09 09:10 |
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Publish the Manuscript Online |
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2025-07-22 09:23 |
| ISSN |
2218-4333 (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 |
Minireviews |
| Article Title |
Edge learning applications in the prediction and classification of combined hepatocellular-cholangiocarcinoma: A comprehensive narrative review
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Sami Akbulut and Cemil Colak |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Sami Akbulut, Department of Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com |
| Key Words |
Edge learning; Combined hepatocellular-cholangiocarcinoma; Artificial intelligence; Disease prediction; Diagnostic accuracy |
| Core Tip |
Edge learning presents a novel approach in combined hepatocellular-cholangiocarcinoma diagnosis and classification, leveraging decentralized artificial intelligence for real-time processing and enhanced data privacy. Unlike traditional cloud-based artificial intelligence, edge learning enables on-site analysis of histopathological features and medical imaging (computed tomography, magnetic resonance imaging) while reducing latency and bandwidth usage. This review explores its technical integration, including federated learning, deep learning optimizations (convolutional neural networks, pruning, quantization), and privacy-preserving artificial intelligence frameworks. By overcoming challenges like diagnostic complexity and data security, edge learning enhances clinical decision-making, treatment planning, and diagnostic accuracy, offering a transformative potential in precision oncology and liver cancer management. |
| Publish Date |
2025-07-22 09:23 |
| Citation |
<p>Akbulut S, Colak C. Edge learning applications in the prediction and classification of combined hepatocellular-cholangiocarcinoma: A comprehensive narrative review. <i>World J Clin Oncol</i> 2025; 16(7): 107246</p> |
| URL |
https://www.wjgnet.com/2218-4333/full/v16/i7/107246.htm |
| DOI |
https://dx.doi.org/10.5306/wjco.v16.i7.107246 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
