Enhancing Newborn Health Assessment: Ultrasound-based Body Composition Prediction Using Deep Learning Techniques.
Authors
Affiliations (6)
Affiliations (6)
- Department of Engineering, Boston College, Chestnut Hill, MA, USA.
- Department of Computer Science, Boston College, Chestnut Hill, MA, USA.
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
- Department of Pediatrics, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
- Connell School of Nursing, Boston College, Chestnut Hill, MA, USA.
- Department of Engineering, Boston College, Chestnut Hill, MA, USA; Connell School of Nursing, Boston College, Chestnut Hill, MA, USA. Electronic address: [email protected].
Abstract
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. 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. 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 kg<sup>2</sup>, FFM: 0.0073 kg<sup>2</sup>), 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 kg<sup>2</sup>, RMSE = 0.0486 kg, MAE = 0.0378 kg). Grad-CAM revealed muscle regions as key contributors to FM and FFM predictions. 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.