Ultrasound-Based Estimation of Hepatic Steatosis in Pediatric MASLD

November 23, 2025 updated by: Jae Won Choi

Prospective Validation of an AI-Driven Ultrasound-Based Method for Estimating Hepatic Steatosis Using MRI-Derived Fat Fraction as Reference in Pediatric Metabolic Dysfunction-Associated Steatotic Liver Disease

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Actual)

50

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • South Korea
      • Seoul, South Korea, 03080
        • Seoul National University Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • participants aged 8 to 18 years clinically indicated for liver ultrasound examination to evaluate hepatic steatosis
  • participants with suspected or known metabolic dysfunction-associated steatotic liver disease (MASLD)
  • able to understand the study purpose and provide written informed consent (from both participant and legal guardian).
  • agree to undergo same-day liver MRI examination in addition to the ultrasound

Exclusion Criteria:

  • unable to cooperate with imaging procedures
  • parent or legal guardian unable to understand the study explanation
  • contraindications to MRI
  • determined by the investigator to be otherwise unsuitable for participation after consultation

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Liver Ultrasound and MRI
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Agreement Between AI-Predicted Ultrasound Fat Fraction (AI-USFF) and MRI Proton Density Fat Fraction (MRI-PDFF)
Time Frame: At time of imaging (single visit)

Reference standard: MRI-PDFF (percentage)

- intraclass correlation coefficient (ICC)
At time of imaging (single visit)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Correlation Between AI-USFF and MRI-PDFF
Time Frame: At time of imaging (single visit)

Reference standard: MRI-PDFF (percentage)

- Pearson correlation coefficient (r)
At time of imaging (single visit)
Diagnostic Performance of AI-USFF for MRI-Based Hepatic Steatosis Grades
Time Frame: At time of imaging (single visit)
The diagnostic performance of AI-USFF for detecting mild, moderate, and severe hepatic steatosis, as defined by MRI-PDFF thresholds, will be evaluated using area under the receiver operating characteristic curve (AUC).
At time of imaging (single visit)
Inter-Vendor Reproducibility of AI-USFF
Time Frame: At time of imaging (single visit)
Reproducibility of AI-USFF across the three ultrasound systems are assessed using the intraclass correlation coefficient (ICC [2,k]).
At time of imaging (single visit)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Jae Won Choi

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 31, 2024

Primary Completion (Actual)

September 23, 2025

Study Completion (Actual)

September 23, 2025

Study Registration Dates

First Submitted

November 23, 2025

First Submitted That Met QC Criteria

November 23, 2025

First Posted (Estimated)

December 4, 2025

Study Record Updates

Last Update Posted (Estimated)

December 4, 2025

Last Update Submitted That Met QC Criteria

November 23, 2025

Last Verified

November 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SNUH-2312-148-1496

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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