A machine learning approach to using ultrasound for body composition and nutritional status assessment in newborns: a pilot study protocol

Abstract

This study will determine the feasibility of integrating ultrasound-based body composition assessment into neonatal clinical workflows as a potential future application. We will evaluate protocol adherence, scan reliability, and clinician and family acceptability to guide further protocol optimization. Findings will inform the design of a larger-scale study and contribute to refining AI models for clinical use. Ultimately, this approach aims to improve the accessibility, accuracy, and efficiency of body composition assessments, particularly in low-resource settings, where it could enable frontline healthcare workers to perform these assessments without specialized training, improving care for vulnerable infants.