Yodoshi T. Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature. World J Gastroenterol 2025; 31(36): 112217 [DOI: 10.3748/wjg.v31.i36.112217]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Sep 28, 2025; 31(36): 112217 Published online Sep 28, 2025. doi: 10.3748/wjg.v31.i36.112217
Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature
Toshifumi Yodoshi
Toshifumi Yodoshi, Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
Author contributions: Yodoshi T contributed to the concept, design, manuscript writing, and editing, as well as the review of the literature.
Conflict-of-interest statement: The author declares no conflicts of interest.
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/
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
Received: July 22, 2025 Revised: August 14, 2025 Accepted: September 3, 2025 Published online: September 28, 2025 Processing time: 60 Days and 4.9 Hours
Core Tip
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.
