Digital twins in pediatric metabolic dysfunction-associated steatotic liver disease: Developmentally referenced multimodal risk stratification

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in children, but its course is heterogeneous and current risk tools are largely static and population-based. No validated pediatric MASLD digital twin currently exists, and no prior synthesis has defined how digital-twin methodology should be adapted to childhood, when growth and puberty change the interpretation of clinical data. This narrative review evaluates digital-twin models and candidate inputs for pediatric MASLD, including noninvasive diagnostics, imaging, body composition, molecular and genetic markers, lifestyle, and social context. We explicitly distinguish direct pediatric MASLD evidence from adult MASLD or metabolic evidence, evidence from other pediatric diseases, and generic digital-twin and artificial-intelligence methodology. We then propose an author-derived, investigational three-layer architecture: Longitudinal multimodal inputs; an age- and puberty-referenced twin engine; and clinician-facing candidate outputs. Initial research use should focus on association-based prognostic monitoring and validation of modular components. Adaptive follow-up and individualized risk estimates require prospective validation, while causal treatment-effect simulation is a separate and more demanding objective. Clinical deployment would require interoperable electronic health record data, prospective multicenter validation, transparent reporting across subgroups, explainability, family-centered consent and assent, cost-effective implementation, and regulatory oversight of adaptive software. The near-term priority is rigorous evaluation of a minimum viable core model rather than deployment of a complete twin. No evidence-based timeline for clinical implementation can yet be specified.

Keywords: Metabolic dysfunction-associated steatotic liver disease; Digital twin; Pediatrics; Risk stratification; Body composition; Precision medicine; Machine learning; Longitudinal modeling

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.