Developing a Deep Learning Approach for Automated Body Composition Prediction in Newborns Using Ultrasound Images

Measurements of human body composition such as fat mass (FM) and fat-free mass (FFM) are critical for studying malnutrition and the effects of nutritional interventions. This study introduces research toward a novel ultrasound scanning protocol combined with a deep learning analysis pipeline for predicting body composition.

We analyzed a clinical dataset of 65 premature infants, consisting of ultrasound images from three anatomical locations (biceps, abdomen, and quadriceps), and ground truth FM and FFM from air displacement plethysmography (ADP). Our investigation focused on determining: 1) the optimal data processing methods for this application; 2) suitable baseline deep learning models for prediction to guide our learning strategy; and 3) the anatomical locations and image regions most predictive of FM and FFM.

We demonstrate that: 1) pre-processing techniques such as denoising, median filtering, and data augmentation enhance performance; 2) by employing a modified EfficientNet-B1 architecture, we achieve fully automatic body composition predictions from ultrasound images; 3) images obtained from combinations of biceps and quadriceps, as well as biceps, quadriceps, and abdomen scanning locations, resulted in mean absolute percent error (MAPE) values of 26.1% and 25.32%, respectively. Finally, sensitivity analysis shows that FM and FFM prediction are influenced by different body parts, as well as adipose and muscle tissue thickness.

This study represents the first demonstration of deep learning for automated human body composition prediction from ultrasound images and lays a critical foundation for a novel ultrasound scanning and interpretation protocol to assess malnutrition.