Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




New Approach Combining Ultrasound Imaging and AI Doubles Accuracy at Detecting Fetal Heart Flaws in the Womb

By MedImaging International staff writers
Posted on 28 May 2021
Image: An ultrasound image shows a normal fetus with relevant heart structures precisely highlighted (Photo courtesy of Rima Arnaout)
Image: An ultrasound image shows a normal fetus with relevant heart structures precisely highlighted (Photo courtesy of Rima Arnaout)
Researchers have found a way to double doctors’ accuracy in detecting the vast majority of complex fetal heart defects in utero by combining routine ultrasound imaging with machine-learning computer tools.

The team of researchers from University of California, San Francisco (UCSF; San Francisco, CA, USA) trained a group of machine-learning models to mimic the tasks that clinicians follow in diagnosing complex congenital heart disease (CHD). Worldwide, humans detect as few as 30% to 50% of these conditions before birth. However, the combination of human-performed ultrasound and machine analysis allowed the researchers to detect 95% of CHD in their test dataset. Diagnosis of fetal heart defects, in particular, can improve newborn outcomes and enable further research on in utero therapies, the researchers said.

The UCSF team trained the machine tools to mimic clinicians’ work in three steps. First, they utilized neural networks to find five views of the heart that are important for diagnosis. Then, they again used neural networks to decide whether each of these views was normal or not. Then, a third algorithm combined the results of the first two steps to give a final result of whether the fetal heart was normal or abnormal.

“We hope this work will revolutionize screening for these birth defects,” said UCSF cardiologist Rima Arnaout, MD, a member of the UCSF Bakar Computational Health Sciences Institute, the UCSF Center for Intelligent Imaging, and a Chan Zuckerberg Biohub Intercampus Research Award Investigator. “Our goal is to help forge a path toward using machine learning to solve diagnostic challenges for the many diseases where ultrasound is used in screening and diagnosis.”

Related Links:
University of California, San Francisco

Digital X-Ray Detector Panel
Acuity G4
Breast Localization System
MAMMOREP LOOP
New
Mobile X-Ray System
K4W
New
MRI System
nanoScan MRI 3T/7T

Channels

Nuclear Medicine

view channel
Image: Perovskite crystal boules are grown in carefully controlled conditions from the melt (Photo courtesy of Mercouri Kanatzidis/Northwestern University)

New Camera Sees Inside Human Body for Enhanced Scanning and Diagnosis

Nuclear medicine scans like single-photon emission computed tomography (SPECT) allow doctors to observe heart function, track blood flow, and detect hidden diseases. However, current detectors are either... Read more

General/Advanced Imaging

view channel
Image: The Angio-CT solution integrates the latest advances in interventional imaging (Photo courtesy of Canon Medical)

Cutting-Edge Angio-CT Solution Offers New Therapeutic Possibilities

Maintaining accuracy and safety in interventional radiology is a constant challenge, especially as complex procedures require both high precision and efficiency. Traditional setups often involve multiple... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.