MASLD, Pediatric
Conditions
Keywords
MASLD, NAFLD, Fatty Liver, Pediatrics
Brief 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.
Interventions
DIAGNOSTIC_TESTUltrasound 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
Jae Won Choi
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
8 Years to 18 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Agreement Between AI-Predicted Ultrasound Fat Fraction (AI-USFF) and MRI Proton Density Fat Fraction (MRI-PDFF) | At time of imaging (single visit) | Reference standard: MRI-PDFF (percentage) \- intraclass correlation coefficient (ICC) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Correlation Between AI-USFF and MRI-PDFF | At time of imaging (single visit) | Reference standard: MRI-PDFF (percentage) \- Pearson correlation coefficient (r) |
| Diagnostic Performance of AI-USFF for MRI-Based Hepatic Steatosis Grades | 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). |
| Inter-Vendor Reproducibility of AI-USFF | At time of imaging (single visit) | Reproducibility of AI-USFF across the three ultrasound systems are assessed using the intraclass correlation coefficient (ICC \[2,k\]). |
Countries
South Korea
Outcome results
None listed