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Ultrasound-Based Estimation of Hepatic Steatosis in Pediatric MASLD

NCT07265011 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 50

Last updated 2025-12-04

No results posted yet for this study

Summary

The purpose of this study is to validate an artificial intelligence (AI)-based algorithm that estimates hepatic steatosis using ultrasound (US) B-mode images in pediatric participants with metabolic dysfunction-associated steatotic liver disease (MASLD). The MRI proton density fat fraction (MRI-PDFF) serves as the reference standard for hepatic fat quantification.

Conditions

  • MASLD
  • Pediatric

Interventions

DIAGNOSTIC_TEST

Ultrasound and MRI

Participants undergo same-day liver imaging including conventional B-mode ultrasound, quantitative ultrasound, and magnetic resonance imaging (MRI). Conventional Ultrasound: B-mode imaging performed on three ultrasound systems (Canon Aplio i800, Philips EPIQ, and Supersonic AIXPLORER) to acquire grayscale liver images for artificial intelligence (AI) analysis. Quantitative Ultrasound: Attenuation imaging (ATI) and shear wave elastography/dispersion measurements performed to assess hepatic fat and stiffness. MRI: Proton density fat fraction (PDFF) measurement used as the reference standard for hepatic steatosis quantification. All imaging is performed on the same day for each participant to ensure temporal consistency across modalities and vendors.

Sponsors & Collaborators

  • Jae Won Choi

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
8 Years
Max Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-12-31
Primary Completion
2025-09-23
Completion
2025-09-23

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07265011 on ClinicalTrials.gov