Scholarlink

<p>Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) affects 30–50% of obese children, yet accurate non-invasive quantification remains challenging. While magnetic resonance imaging-proton density fat fraction (MRI-PDFF) represents the reference standard, its limited accessibility necessitates alternative approaches. Forty pediatric patients (age 12.16 ± 2.56 years) referred for MASLD were prospectively enrolled for same-day ultrasound radiofrequency (RF) data acquisition and MRI-PDFF examination. Two artificial intelligence (AI) approaches using multiple input combinations of RF data, ultrasound-guided attenuation parameters (UGAP), and clinical parameters were developed for non-invasive pediatric liver fat quantification: radiomics-based models and deep learning models. The best radiomics model (XGBoost) and the best deep learning model (Mod-MHDNet) achieved optimal performance with multimodal inputs (R<sup>2</sup> = 0.81 and 0.76, respectively). Bland–Altman analysis demonstrated excellent agreement with MRI-PDFF, with a mean bias of &lt; 0.4% points for both approaches. AI analysis of ultrasound RF data enables accurate and accessible quantification of pediatric liver fat, offering a practical alternative for MASLD evaluation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-powered ultrasound radiofrequency analysis for non-invasive pediatric liver fat quantification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

查看全文
  • Gayoung Choi,
  • Sungwon Ham,
  • Bo-Kyung Je,
  • Minsoo Shin

摘要

Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) affects 30–50% of obese children, yet accurate non-invasive quantification remains challenging. While magnetic resonance imaging-proton density fat fraction (MRI-PDFF) represents the reference standard, its limited accessibility necessitates alternative approaches. Forty pediatric patients (age 12.16 ± 2.56 years) referred for MASLD were prospectively enrolled for same-day ultrasound radiofrequency (RF) data acquisition and MRI-PDFF examination. Two artificial intelligence (AI) approaches using multiple input combinations of RF data, ultrasound-guided attenuation parameters (UGAP), and clinical parameters were developed for non-invasive pediatric liver fat quantification: radiomics-based models and deep learning models. The best radiomics model (XGBoost) and the best deep learning model (Mod-MHDNet) achieved optimal performance with multimodal inputs (R2 = 0.81 and 0.76, respectively). Bland–Altman analysis demonstrated excellent agreement with MRI-PDFF, with a mean bias of < 0.4% points for both approaches. AI analysis of ultrasound RF data enables accurate and accessible quantification of pediatric liver fat, offering a practical alternative for MASLD evaluation.