Enhancing Newborn Health Assessment: Ultrasound-based Body Composition Prediction Using Deep Learning Techniques.

OBJECTIVE

This study investigates the feasibility of deep learning to predict body composition with ultrasound, specifically fat mass (FM) and fat-free mass (FFM), to improve newborn health assessments.

METHODS

We analyzed 721 ultrasound images of the biceps, quadriceps and abdomen from 65 pre-term infants. A deep learning model incorporating a modified U-Net architecture was developed to predict FM and FFM using air displacement plethysmography as ground truth labels for training. Model performance was assessed using mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE), along with Bland-Altman plots to evaluate mean bias and limits of agreement. We tested different image combinations to determine the contribution of anatomical regions. Grad-CAM was applied to identify image regions with the strongest influence on predictions.

RESULTS

Combining biceps, quadriceps and abdominal ultrasound images to predict whole-body composition showed strong agreement with ground truth values, with low MAE (FM: 0.0145 kg, FFM: 0.0794 kg), MSE (FM: 0.0003 kg2, FFM: 0.0073 kg2), RMSE (FM: 0.0183 kg, FFM: 0.0854 kg) and MAPE (FM: 2.65%, FFM: 8.40%). Using only abdominal images for prediction improved FFM performance (MAPE = 4.62%, MSE = 0.0041 kg2, RMSE = 0.0486 kg, MAE = 0.0378 kg). Grad-CAM revealed muscle regions as key contributors to FM and FFM predictions.

CONCLUSION

Deep learning provides a promising approach to predicting body composition with ultrasound and could be a valuable tool for assessing nutritional status in neonatal care.