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

GE Healthcare

GE Healthcare provides medical imaging and information technologies, medical diagnostics, patient monitoring systems,... read more Featured Products: More products

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

AI-Based Tool Assists Interventional Electrophysiology

By HospiMedica International staff writers
Posted on 21 Dec 2020
Print article
Image: The VX1 AI software mapping system (Photo courtesy of Volta Medical)
Image: The VX1 AI software mapping system (Photo courtesy of Volta Medical)
An artificial intelligence (AI) algorithm helps annotate 3D anatomical and electrical maps of the heart during atrial fibrillation (AF) or atrial tachycardia (AT) events.

The Volta Medical (Marseille, France) VX1 AI software mapping system is an intuitive, user-friendly platform with an innovative deep learning algorithm that analyzes a patient's electrograms in order to locate heart regions harboring a specific abnormality called spatiotemporal dispersion, which is evident during AF or AT. The system works with two acquisition systems, the Boston Scientific (Natick, MA, USA) LabSystem Pro, and the GE Healthcare (GE, Little Chalfont, United Kingdom) MacLab CardioLab.

A cable connects the selected data acquisition system with an Advantech PCI-1713U analog-to-digital converter, which transmits the acquired information to a computer outside the sterile operating room area that hosts the VX1 AI software. The software then analyzes the patient’s electrograms and cues operators in real-time to spatiotemporal dispersion events, as well as cycle length, estimated from electrograms recorded with the mapping and the coronary sinus catheters. The results of the analysis are graphically presented on the computer display.

“Our ultimate goal is to offer an alternative to a lifetime of medication that can have problematic side effects in some patients, while bringing a better quality of life to those who suffer from bothersome daily disease symptoms,” said Jérôme Kalifa, MD, co-founder of Volta Medical. “This is what is generating increasing enthusiasm around the potential of our solution, which is compatible with most multipolar catheters and technologies currently used in operating rooms or electrophysiology labs.”

“AF represents a major challenge in cardiology due to the complexities associated with identification, localization, and treatment of the pathological zones that cause and perpetuate this abnormal heart rhythm,” said Seth Goldbarg, MD, director of cardiac electrophysiology at Weill Cornell Medical College (Flushing, NY, USA). “We are excited to be part of the further studies taking place with this AI software, as the Volta system may provide a major step forward in the effective approach to ablation of persistent atrial fibrillation.”

AF occurs when the heart's two upper chambers beat erratically. In one form, paroxysmal AF, patients have bouts of erratic beats that begin spontaneously and usually last less than a week. AAD can control the heart rhythm and symptoms of AF, but many patients do not respond well. AF can lead to serious adverse events such as thrombi travelling from the heart to obstruct arteries supplying the brain, causing stroke, or other parts of the body causing tissue damage.

Related Links:
Volta Medical
Boston Scientific
GE Healthcare




Print article

Channels

Business

view channel
Illustration

Machine Learning Algorithm Identifies Deteriorating Patients in Hospital Who Need Intensive Care

Researchers have developed a machine learning algorithm that could significantly improve clinicians’ ability to identify hospitalized patients whose condition is deteriorating to the extent that they need... Read more
Copyright © 2000-2021 Globetech Media. All rights reserved.