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Ultrasound for assessing paediatric body composition and nutritional status: Scoping review and future directions

Bryan J Ranger1,2, Allison Lombardi2, Susie Kwon2

  • 1Department of Engineering, Boston College, Chestnut Hill, Massachusetts, USA.

Acta Paediatrica (Oslo, Norway : 1992)
|September 19, 2024
PubMed

Insights

Point-of-care ultrasound shows promise for assessing pediatric body composition and nutritional status. Further research with diverse cohorts and AI integration is recommended for improved accuracy and efficiency.

Area of Science:

  • Pediatric medicine
  • Medical imaging
  • Nutritional science

Background:

  • Assessing body composition and nutritional status in children is crucial for monitoring growth and health.
  • Traditional methods may have limitations in accuracy, accessibility, or invasiveness.
  • Ultrasound technology offers a non-invasive, potentially portable alternative for these assessments.

Purpose of the Study:

  • To conduct a scoping review on the utility of ultrasound for measuring body composition and nutritional status in the pediatric population.
  • To summarize existing literature, identify research gaps, and suggest future research directions.

Main Methods:

  • A systematic scoping review was performed following PRISMA Extension for Scoping Reviews guidelines.
  • Included screening of titles and abstracts, followed by full-text review and data extraction.
  • Synthesis of data from 34 relevant articles.

Main Results:

  • Ultrasound has been utilized to assess pediatric body composition and bone properties.
  • Significant literature gaps exist, including limited studies on ultrasound performance and sample characteristics.
  • Current methods often rely on manual measurements, and sample diversity is limited.

Conclusions:

  • Point-of-care ultrasound demonstrates considerable potential for pediatric body composition and nutritional status assessment.
  • Further research is essential, focusing on larger, diverse cohorts and longitudinal studies.
  • Development of AI for automated data interpretation is key to enhancing accuracy and efficiency.
Abstract

Keywords:
artificial intelligencebody compositiongrowth and developmentmachine learningnutritionultrasound imaging

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