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




AI Technology for Automated Assessment of Coronary Angiograms to Reduce Invasive Testing

By MedImaging International staff writers
Posted on 17 May 2023
Print article
Image: Artificial intelligence can reduce invasive testing and improve cardiac diagnostics (Photo courtesy of Freepik)
Image: Artificial intelligence can reduce invasive testing and improve cardiac diagnostics (Photo courtesy of Freepik)

Coronary heart disease is the primary cause of death in adults globally. Coronary angiography is a standard diagnostic procedure that influences virtually all relevant clinical choices, from medication prescriptions to coronary bypass surgery. In many instances, quantifying the left ventricular ejection fraction (LVEF) at the time of coronary angiography is essential for enhancing clinical decisions and treatment plans, especially when the angiography is carried out due to potentially fatal acute coronary syndromes (ACS). As the left ventricle is the main pumping part of the heart, assessing the ejection fraction in this chamber offers crucial details about the percentage of blood leaving the heart with each contraction. Currently, an extra-invasive procedure, known as left ventriculography, is required to measure LVEF during angiography, which involves inserting a catheter into the left ventricle and injecting a contrast dye. This procedure carries additional risks and increases contrast exposure. Now, researchers have developed automated assessment of coronary angiograms to reduce risk and minimize the need for invasive testing.

In a new study, researchers at University of California San Francisco (San Francisco, CA, USA) and the Montreal Heart Institute (Montreal, Canada) aimed to examine whether deep neural networks (DNNs), a type of AI algorithm, could predict cardiac pump function from standard angiogram videos. They created and tested a DNN named CathEF to estimate LVEF from coronary angiograms of the heart's left side. The team conducted a cross-sectional study of 4042 adult angiograms matched with corresponding transthoracic echocardiograms (TTEs) from 3679 UCSF patients. They trained a video-based neural network to estimate reduced LVEF (equal to or less than 40%) and to predict the LVEF percentage from standard angiogram videos of the left coronary artery.

The findings indicated that CathEF accurately predicted LVEF, displaying strong correlations with echocardiographic LVEF measurements, which is the typical noninvasive clinical method. The model was also externally validated in real-world angiograms. It performed well across diverse patient demographics and clinical conditions, including acute coronary syndromes and varying degrees of renal function - groups of patients who may be less suitable for the standard left ventriculogram procedure. The researchers are now conducting further research to test this algorithm at the point of care and assess its influence on the clinical workflow in patients experiencing heart attacks. To that end, they have initiated a multi-center prospective validation study in patients with ACS to compare the performance of CathEF and the left ventriculogram with TTEs performed within 7 days of ACS.

“This work demonstrates that AI technology has the potential to reduce the need for invasive testing and improve the diagnostic capabilities of cardiologists, ultimately improving patient outcomes and quality of life,” said senior author and UCSF cardiologist Geoff Tison, MD, MPH.

Related Links:
UC San Francisco 
Montreal Heart Institute 

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Ultrasound System
Acclarix AX9
New
PACS Workstation
CHILI Web Viewer
Under Table Shield
3 Section Double Pivot Under Table Shield

Print article

Channels

Nuclear Medicine

view channel
Image: The AI system uses scintigraphy imaging for early diagnosis of cardiac amyloidosis (Photo courtesy of 123RF)

AI System Automatically and Reliably Detects Cardiac Amyloidosis Using Scintigraphy Imaging

Cardiac amyloidosis, a condition characterized by the buildup of abnormal protein deposits (amyloids) in the heart muscle, severely affects heart function and can lead to heart failure or death without... Read more

General/Advanced Imaging

view channel
Image: The Cinematic Reality app enables interaction with realistic renderings of human anatomy (Photo courtesy of Siemens)

AR Application Turns Medical Scans Into Holograms for Assistance in Surgical Planning

Siemens Healthineers (Erlangen, Germany) has launched an app designed for Apple Vision Pro that allows users including surgeons, medical students, or patients to view immersive, interactive holograms of... 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

Industry News

view channel
Image: Samsung Medison CEO Mr. Yongkwan Kim and Bracco Imaging CEO Dr. Fulvio Renoldi Bracco endorsed a MoU agreement (Photo courtesy of Bracco Group)

Samsung and Bracco Enter Into New Diagnostic Ultrasound Technology Agreement

Samsung Medison (Seoul, South Korea) and Bracco Imaging (Milan, Italy) have entered into a Memorandum of Understanding (MoU) agreement to pioneer a new area for diagnostic ultrasound devices and contrast agents.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.