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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Hepatol. Oct 27, 2026; 18(10): 125486
Published online Oct 27, 2026. doi: 10.4254/wjh.125486
Digital twins in pediatric metabolic dysfunction-associated steatotic liver disease: Developmentally referenced multimodal risk stratification
Toshifumi Yodoshi
Toshifumi Yodoshi, Division of Gastroenterology, Hepatology, Pancreatology and Nutrition, University of Iowa Stead Family Children’s Hospital, Iowa, IA 52242, United States
Author contributions: Yodoshi T conceived the review, performed the literature search, verified and synthesized the evidence, developed the conceptual framework and figure, drafted and critically revised the manuscript, and approved the final version.
AI contribution statement: AI tools were not used during manuscript preparation.
Conflict-of-interest statement: The author declares no conflicts of interest related to this article.
Corresponding author: Toshifumi Yodoshi, MD, PhD, Assistant Professor, Division of Gastroenterology, Hepatology, Pancreatology and Nutrition, University of Iowa Stead Family Children’s Hospital, No. 200 Hawkins Drive, Iowa, IA 52242, United States. toshifumi-yodoshi@uiowa.edu
Received: July 8, 2026
Revised: August 10, 2026
Accepted: September 8, 2026
Published online: October 27, 2026
Processing time: 105 Days and 10.6 Hours
Core Tip

Core Tip: Pediatric metabolic dysfunction-associated steatotic liver disease is dynamic, but routine monitoring relies heavily on isolated measurements. No validated pediatric metabolic dysfunction-associated steatotic liver disease (MASLD) digital twin currently exists. This review separates direct pediatric MASLD evidence from adult, other-pediatric, and generic digital-twin evidence and proposes an author-derived, investigational architecture. A minimum viable core should begin with routinely available developmental, anthropometric, laboratory, treatment, and clinically obtained imaging data, with advanced imaging, body composition, genetics, multi-omics, microbiome, and digital phenotyping added only as separately validated modules. Near-term research should emphasize association-based prognostic monitoring, explicit uncertainty, missing-not-at-random data, false alerts and false reassurance, and silent prospective validation. Treatment-effect simulation requires separate causal validation and should not be inferred from ordinary risk prediction.

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