BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
9/19/2025 7:57:22 AM | Browse: 457 | Download: 384
 |
Received |
|
2025-07-22 03:51 |
|
Peer-Review Started |
|
2025-07-22 03:51 |
|
First Decision by Editorial Office Director |
|
2025-08-14 09:36 |
|
Return for Revision |
|
2025-08-14 09:36 |
|
Revised |
|
2025-08-14 13:53 |
 |
Publication Fee Transferred |
|
|
|
Second Decision by Editor |
|
2025-09-03 02:46 |
|
Second Decision by Editor-in-Chief |
|
|
|
Final Decision by Editorial Office Director |
|
2025-09-03 06:16 |
|
Articles in Press |
|
2025-09-03 06:16 |
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2025-09-11 06:42 |
|
Publish the Manuscript Online |
|
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
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| 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
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Toshifumi Yodoshi |
| ORCID |
|
| Funding Agency and Grant Number |
|
| 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 |
Yodoshi T. Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature. World J Gastroenterol 2025; 31(36): 112217 |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i36/112217.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i36.112217 |
All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.
