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 hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Ultrasound Tools Provides Confidence in Cardiac Care

By MedImaging International staff writers
Posted on 20 Sep 2015
Print article
Image: Cardiac model used in the Philips HeartModelA.I (Photo courtesy of Royal Philips).
Image: Cardiac model used in the Philips HeartModelA.I (Photo courtesy of Royal Philips).
A new anatomically intelligent ultrasound (AIUS) tool brings advanced quantification, automated 3-D views, and robust reproducibility to echocardiology.

The Royal Philips (Amsterdam, The Netherlands) HeartModelA.I is designed to automatically detect, segment, and quantify the left atrium (LA) and left ventricle (LV) volume and ejection fraction (EF). The model-based segmentation algorithm is based on prior knowledge of the general structural layout of the heart, how heart location varies within an image, the ways in which the heart shape varies, and the ways in which the heart is imaged using ultrasound. This prior information is what enables the HeartModelA.I. to adapt the model to hearts typically seen in a clinical scenario.

Clinically, HeartModelA.I is designed to detect two LV endocardial borders, at end-diastole (ED) and end-systole (ES). The two endocardial borders mark the inner and outer extents of the myocardial tissue at the blood-tissue interface and at the interface of the compacted myocardium. By thus segmenting the inner and outer extents of the myocardial tissue, an intermediate location can be more robustly defined across a wide range of heart shapes and image quality 3-6 times faster than current methods that are based on using 3D measurements.

When editing of the borders is necessary, the user has two editing options available - a global or regional edit. The global edit consists of adjusting the ED or ES slider value, or relative location of the single LV endocardial border relative to the inner and outer borders that were automatically detected by the algorithm. Regional editing involves adjusting the border on a more localized basis via control points placed along the contour, allows the user to use the application even on hearts exhibiting a very unique or irregular shape.

“Health systems are constantly looking for solutions to provide the most efficient and effective way to help clinicians make confident diagnosis,” said Vitor Rocha, CEO of ultrasound at Philips. “Conventional echocardiograms can be very time consuming. By combining AIUS with the power of HeartModelA.I, we’re able to deliver technology that helps simplify a complicated exam and makes it more reproducible.”

Ultrasound provides a cost-effective, robust imaging modality to measure cardiac function without radiation exposure. Typically, two dimensional (2D) echocardiographic images are used to measure LA or LV volume and EF output, but these measurements rely on making assumptions about the 3-D shape of the heart based only on what is seen in the 2-D image, an assumption that can significantly impact the measurements.

Related Links:

Royal Philips


New
Gold Member
X-Ray QA Meter
T3 AD Pro
New
Digital Radiographic System
OMNERA 300M
Fixed X-Ray System (RAD)
Allengers 325 - 525
New
40/80-Slice CT System
uCT 528

Print article
Radcal

Channels

Radiography

view channel
Image: The new X-ray detector produces a high-quality radiograph (Photo courtesy of ACS Central Science 2024, DOI: https://doi.org/10.1021/acscentsci.4c01296)

Highly Sensitive, Foldable Detector to Make X-Rays Safer

X-rays are widely used in diagnostic testing and industrial monitoring, from dental checkups to airport luggage scans. However, these high-energy rays emit ionizing radiation, which can pose risks after... Read more

MRI

view channel
Image: Artificial intelligence models can be trained to distinguish brain tumors from healthy tissue (Photo courtesy of 123RF)

AI Can Distinguish Brain Tumors from Healthy Tissue

Researchers have made significant advancements in artificial intelligence (AI) for medical applications. AI holds particular promise in radiology, where delays in processing medical images can often postpone... Read more

Nuclear Medicine

view channel
Image: Example of AI analysis of PET/CT images (Photo courtesy of Academic Radiology; DOI: 10.1016/j.acra.2024.08.043)

AI Analysis of PET/CT Images Predicts Side Effects of Immunotherapy in Lung Cancer

Immunotherapy has significantly advanced the treatment of primary lung cancer, but it can sometimes lead to a severe side effect known as interstitial lung disease. This condition is characterized by lung... Read more

General/Advanced Imaging

view channel
Image: Cleerly offers an AI-enabled CCTA solution for personalized, precise and measurable assessment of plaque, stenosis and ischemia (Photo courtesy of Cleerly)

AI-Enabled Plaque Assessments Help Cardiologists Identify High-Risk CAD Patients

Groundbreaking research has shown that a non-invasive, artificial intelligence (AI)-based analysis of cardiac computed tomography (CT) can predict severe heart-related events in patients exhibiting symptoms... 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.