• AI
  • Molecular Imaging
  • CT
  • X-Ray
  • Ultrasound
  • MRI
  • Facility Management
  • Mammography

Emerging AI Advances in Cardiac Imaging


In a recent video interview, David Ouyang, M.D., shared insights from two recent studies he co-authored on the use of artificial intelligence (AI) to improve initial assessment of left ventricular ejection fraction (LVEF) on echocardiography and ascertain cardiac risks associated with changes in the left ventricle sphericity index seen on magnetic resonance imaging (MRI).

When evaluating an artificial intelligence (AI) model for initial assessment of left ventricular ejection fraction (LVEF) on echocardiograms, David Ouyang, M.D., said he was genuinely surprised that cardiologists had greater confidence in initial AI assessments than those provided by cardiac sonographers with an average of 14.1 years of experience.

In the randomized trial, recently published in Nature, cardiologists revised 27.2 percent of initial LVEF assessments by sonographers in comparison to 16.8 percent of AI assessments.

“We initially thought of the tool as kind of a streamlining tool where it speeds things up. We actually designed the trial as a non-inferior trial. We weren’t expecting AI to be better than sonographers. Hopefully, it would be equivalent,” noted Dr. Ouyang, who is affiliated with the Department of Cardiology at the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine at the Cedars-Sinai Medical Center in Los Angeles. “We were pleasantly surprised that AI actually was superior. This really speaks to the high level of precision that automation delivers.”

(Editor’s note: For related content, see “Study Finds AI More Effective Than Sonographer Interpretation of Cardiac Function on Echocardiograms,” “Can AI Improve Triage of CT Pulmonary Angiography Exams for Acute PE?” and “AI Platform for Ultrasound Detection of Cardiac Amyloidosis Gets FDA Breakthrough Device Designation.”)

In a recent interview, Dr. Ouyang also discussed another study that employed deep learning segmentation of cardiac magnetic resonance imaging (MRI) to examine how variations in left ventricle sphericity can impact cardiovascular risks. Based on their findings from a large cohort of nearly 39,000 participants from the UK Biobank, Dr. Ouyang and colleagues found that one standard deviation increase in the left ventricle sphericity index led to a 47 percent increased risk for cardiomyopathy and a 20 percent higher risk of atrial fibrillation.

For more insights from Dr, Ouyang, watch the video below.

Related Videos
Improving the Quality of Breast MRI Acquisition and Processing
Can Fiber Optic RealShape (FORS) Technology Provide a Viable Alternative to X-Rays for Aortic Procedures?
Making the Case for Intravascular Ultrasound Use in Peripheral Vascular Interventions
Can Diffusion Microstructural Imaging Provide Insights into Long Covid Beyond Conventional MRI?
Emerging MRI and PET Research Reveals Link Between Visceral Abdominal Fat and Early Signs of Alzheimer’s Disease
Nina Kottler, MD, MS
The Executive Order on AI: Promising Development for Radiology or ‘HIPAA for AI’?
Practical Insights on CT and MRI Neuroimaging and Reporting for Stroke Patients
Related Content
© 2024 MJH Life Sciences

All rights reserved.