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




Researchers Develop New AI Technology for Heart Attack Prediction

By MedImaging International staff writers
Posted on 18 Sep 2019
Print article
Image: New AI technology could identify people who are at high risk for a fatal heart attack (Photo courtesy of Medscape).
Image: New AI technology could identify people who are at high risk for a fatal heart attack (Photo courtesy of Medscape).
Researchers from the University of Oxford (Oxford, England) have developed a technology using artificial intelligence (AI) that could identify people at high risk of a fatal heart attack at least five years before it strikes. Using machine learning, the researchers have developed a new biomarker, or 'fingerprint', called the fat radiomic profile (FRP), which detects biological red flags in the perivascular space lining blood vessels that supply blood to the heart. It identifies inflammation, scarring and changes to these blood vessels, which are all pointers to a future heart attack.

For their study, the researchers initially used fat biopsies from 167 people undergoing cardiac surgery. They analyzed the expression of genes associated with inflammation, scarring and new blood vessel formation, and matched these to the CCTA scan images to determine which features best indicate changes to the fat surrounding the heart vessels, called perivascular fat. The team then compared the CCTA scans of the 101 people, from a pool of 5,487 individuals, who went on to have a heart attack or cardiovascular death within five years of having a CCTA with matched controls who did not, to understand the changes in the perivascular space, which indicate that someone is at higher risk of a heart attack.

Using machine learning, they developed the FRP fingerprint that captures the level of risk. The more heart scans that are added, the more accurate the predictions will become, and the more information that will become 'core knowledge'. The researchers tested the performance of this perivascular fingerprint in 1,575 people in the SCOT-HEART trial, showing that the FRP had a striking value in predicting heart attacks, above what can be achieved with any of the tools currently used in clinical practice. The team hopes that the powerful technology will enable a greater number of people to avoid a heart attack, and plan to roll it out to health care professionals in the next year.

“By harnessing the power of AI, we've developed a fingerprint to find 'bad' characteristics around people's arteries,” said Professor Charalambos Antoniades, Professor of Cardiovascular Medicine and BHF Senior Clinical Fellow at the University of Oxford. “This has huge potential to detect the early signs of disease, and to be able to take all preventative steps before a heart attack strikes, ultimately saving lives. We genuinely believe this technology could be saving lives within the next year.”

“This research is a powerful example of how innovative use of machine learning technology has the potential to revolutionize how we identify people at risk of a heart attack and prevent them from happening,” said Professor Metin Avkiran, Associate Medical Director at the British Heart Foundation. “This is a significant advance. The new 'fingerprint' extracts additional information about underlying biology from scans used routinely to detect narrowed arteries. Such AI-based technology to predict an impending heart attack with greater precision could represent a big step forward in personalized care for people with suspected coronary artery disease.”

Related Links:
University of Oxford

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Color Doppler Ultrasound System
DRE Crystal 4PX
Advanced Cardiac MRI Analysis Software
3Di Cardiac MR
New
Ultrasound Doppler System
Doppler BT-200

Print article

Channels

Ultrasound

view channel
Image: Structure of the proposed transparent ultrasound transducer and its optical transmittance (Photo courtesy of POSTECH)

Ultrasensitive Broadband Transparent Ultrasound Transducer Enhances Medical Diagnosis

The ultrasound-photoacoustic dual-modal imaging system combines molecular imaging contrast with ultrasound imaging. It can display molecular and structural details inside the body in real time without... Read more

Nuclear Medicine

view channel
Image: PET/CT of a 60-year-old male patient with clinical suspicion of lung cancer (Photo courtesy of EJNMMI Physics)

Early 30-Minute Dynamic FDG-PET Acquisition Could Halve Lung Scan Times

F-18 FDG-PET scans are a way to look inside the body using a special dye, and these scans can be either static or dynamic. Static scans happen 60 minutes after the dye is administered into the body, showing... 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.