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Articles Published Processes
9/19/2025 7:57:22 AM | Browse: 329 | Download: 66
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Received |
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2025-07-22 03:51 |
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Peer-Review Started |
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2025-07-22 03:51 |
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To Make the First Decision |
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Return for Revision |
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2025-08-14 09:36 |
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Revised |
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2025-08-14 13:53 |
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Second Decision |
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2025-09-03 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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2025-09-03 06:16 |
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Articles in Press |
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2025-09-03 06:16 |
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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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2025-09-11 06:42 |
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Publish the Manuscript Online |
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2025-09-19 07:57 |
| 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 |
Gastroenterology & Hepatology |
| Manuscript Type |
Editorial |
| Article Title |
Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Toshifumi Yodoshi |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Toshifumi Yodoshi, MD, PhD, Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229, United States. Toshifumi.Yodoshi@cchmc.org |
| Key Words |
Liver fibrosis; Machine learning; Non-invasive biomarkers; Overfitting; Ethnic diversity; Cost-effectiveness; External validation; Health economics; Metabolic dysfunction-associated steatotic liver disease |
| Core Tip |
Machine-learning models for detecting advanced fibrosis in pediatric metabolic dysfunction-associated steatotic liver disease routinely report striking accuracy, yet three recurring flaws limit clinical impact: Overfitting to single-center datasets, absence of multi-ethnic external validation, and dependence on costly, non-routine biomarkers. We outline a pragmatic roadmap prospective, multi-site cohorts with standardized liver histology, decision-curve and cost-utility analyses, and transparent model explainability to transform promising algorithms into trustworthy tools. Until these steps are fulfilled, the most reliable strategy combines simple serum tests (alanine aminotransferase, platelet-based indices), vibration-controlled transient elastography, and judicious, targeted liver biopsy for indeterminate or high-risk cases. |
| Publish Date |
2025-09-19 07:57 |
| Citation |
<p>Yodoshi T. Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature. <i>World J Gastroenterol</i> 2025; 31(36): 112217</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i36/112217.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i36.112217 |
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