We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

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

Download Mobile App




Breakthrough in Ultrasound Imaging Technique Enables Early Detection of Heart Disease

By MedImaging International staff writers
Posted on 09 Dec 2022
Print article
Image: Artificial intelligence has now been trained to detect pixel patterns in ultrasound images (Photo courtesy of Pexels)
Image: Artificial intelligence has now been trained to detect pixel patterns in ultrasound images (Photo courtesy of Pexels)

Researchers have reported a new breakthrough in ultrasonic imaging methods that can detect microscopic changes in heart structure and function, which may be useful for screening early heart disease using miniaturized ultrasound devices that can be carried in the pocket.

Researchers at Rutgers Robert Wood Johnson Medical School (Rutgers RWJMS, New Brunswick, NJ, USA) and Robert Wood Johnson University Hospital (RWJUH, New Brunswick, NJ, USA) used artificial intelligence (AI) modeling techniques to compile and analyze pixel-based patterns in echocardiogram images of humans to develop expert-level interpretation of cardiac conditions that lead to heart failure. They then used a mouse model of heart failure and discovered that these patterns arise from microscopic changes in heart muscle geometry. Through their analysis, the researchers were able to establish new biological markers, or indicators, for cardiovascular disease, that can help clinicians detect cardiac issues earlier and give them important information they need to plan the appropriate treatment.

“By establishing and analyzing patterns of pixels obtained from the sample echocardiogram images, we were able to predict presence of heart conditions that can cause heart failure,” explained Partho Sengupta, MD, FACC, who is the Henry Rutgers Professor of Cardiology and Chief of the Division of Cardiovascular Disease and Hypertension at RWJMS, and Chief of Cardiology at RWJUH, and is a member of the Combined Medical Group of RWJBarnabas Health and Rutgers Health. “Identifying changes in the heart muscle or cardiovascular function earlier can lead to more proactive interventions and the prevention of serious complications.”

According to Dr. Sengupta, this biomarker can be applied to any current cardiac ultrasound device, including advanced, miniature hand-held point of care ultrasound technology. Essentially, the data is like obtaining an ultrasonic biopsy of the heart tissue, he said.

“This has the potential to give more people access to in-depth, expert analysis in a broad range of settings, leading to faster intervention and prevention of serious cardiac disease,” Dr. Sengupta noted.

Related Links:
Rutgers RWJMS
RWJUH

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Digital Radiography Generator
meX+20BT lite
New
X-Ray Detector
FDR-D-EVO III
Oncology Information System
RayCare

Print article
Radcal

Channels

MRI

view channel
Image: Shorter scan to diagnose prostate cancer can increase availability and reduce cost (Photo courtesy of 123RF)

Two-Part MRI Scan Detects Prostate Cancer More Quickly without Compromising Diagnostic Quality

Prostate cancer ranks as the most prevalent cancer among men. Over the last decade, the introduction of MRI scans has significantly transformed the diagnosis process, marking the most substantial advancement... Read more

Nuclear Medicine

view channel
Image: The radiotheranostic platform employs a MUC16-targeting humanized antibody, huAR9.6 (Photo courtesy of MSK)

New Radiotheranostic System Detects and Treats Ovarian Cancer Noninvasively

Ovarian cancer is the most lethal gynecological cancer, with less than a 30% five-year survival rate for those diagnosed in late stages. Despite surgery and platinum-based chemotherapy being the standard... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... 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 © 2000-2024 Globetech Media. All rights reserved.