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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...

Ultrasound I: Abdominal Ultrasonography

Ultrasound I: Abdominal Ultrasonography

Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...

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Related Experiment Video

Updated: Jan 14, 2026

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research

Published on: June 7, 2024

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

Keshi He¹, Julia Hohenberg², Yi Li²

¹Department of Engineering, Boston College, Chestnut Hill, MA, USA.

Ultrasound in Medicine & Biology

|October 16, 2025

Summary

This summary is machine-generated.

Deep learning accurately predicts infant body composition using ultrasound, offering a non-invasive method for assessing fat mass and fat-free mass to enhance neonatal health assessments.

Keywords:

Body composition prediction Deep learning Malnutrition Newborn and child health Ultrasound imaging

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Published on: March 13, 2021

Area of Science:

Neonatal care
Medical imaging
Biomedical engineering

Background:

Accurate assessment of body composition, including fat mass (FM) and fat-free mass (FFM), is crucial for neonatal health.
Traditional methods for body composition analysis can be invasive or inaccessible in neonatal intensive care units.
Ultrasound imaging presents a potential non-invasive alternative for body composition assessment.

Purpose of the Study:

To investigate the feasibility of using deep learning models with ultrasound images to predict infant body composition (FM and FFM).
To develop and validate a deep learning model for accurate body composition prediction in preterm infants.
To assess the contribution of different anatomical regions in ultrasound images for body composition prediction.

Main Methods:

A deep learning model with a modified U-Net architecture was trained on 721 ultrasound images from 65 preterm infants.
Air displacement plethysmography served as the ground truth for training the model to predict FM and FFM.
Model performance was evaluated using MAE, MSE, RMSE, and MAPE, with Grad-CAM used for image region analysis.

Main Results:

The combined use of biceps, quadriceps, and abdominal ultrasound images demonstrated strong agreement with ground truth values for FM and FFM prediction.
Specific performance metrics included low MAE (FM: 0.0145 kg, FFM: 0.0794 kg) and MAPE (FM: 2.65%, FFM: 8.40%).
Utilizing only abdominal images improved FFM prediction accuracy (MAPE: 4.62%), and Grad-CAM highlighted muscle regions as critical for predictions.

Conclusions:

Deep learning applied to ultrasound imaging is a feasible and promising method for predicting infant body composition.
This approach can serve as a valuable, non-invasive tool for assessing nutritional status in neonatal care.
Further research can optimize the model for broader clinical application in newborn health assessments.