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
Sekisui Diagnostics UK Ltd.

Download Mobile App




AI to Improve Diagnosis of Atrial Fibrillation

By HospiMedica International staff writers
Posted on 10 May 2024
Print article
Image: AI could help physicians detect abnormal heart rhythms earlier (Photo courtesy of 123RF)
Image: AI could help physicians detect abnormal heart rhythms earlier (Photo courtesy of 123RF)

Abnormal heart rhythms frequently arise from—and contribute to—structural abnormalities in the heart. Atrial fibrillation is a specific type of abnormal rhythm that may not be consistently present, often eluding detection during routine medical evaluations. In atrial fibrillation, the synchronization between the heart's upper and lower chambers can intermittently fail, complicating its diagnosis for healthcare professionals. While some individuals experience no symptoms from atrial fibrillation, others may suffer from heart palpitations, fatigue, shortness of breath, dizziness, and other issues that disrupt their everyday activities. If not addressed, atrial fibrillation can lead to severe complications such as stroke and heart failure. Now, researchers have developed an artificial intelligence (AI) program that is capable of identifying atrial fibrillation by analyzing images from echocardiograms—a common diagnostic test that utilizes sound waves to produce images of the heart.

A research team at Cedars-Sinai (Los Angeles, CA, USA) hypothesized that an AI tool designed to interpret echocardiograms could assist medical professionals in recognizing early, subtle cardiac changes in patients with undetected arrhythmias. They trained this AI system using over 100,000 video echocardiograms from patients diagnosed with atrial fibrillation. The system was able to differentiate between echocardiograms depicting normal sinus rhythm and those illustrating irregular rhythms. Additionally, it successfully predicted which patients currently in sinus rhythm were at risk of developing or having a recurrence of atrial fibrillation within the next 90 days. This AI model's performance in assessing the images surpassed traditional methods that predict risk based solely on established risk factors.

“We were able to show that a deep learning algorithm we developed could be applied to echocardiograms to identify patients with a hidden abnormal heart rhythm disorder called atrial fibrillation,” said Neal Yuan, MD, a staff scientist with the Smidt Heart Institute and first and corresponding author of the study. “Atrial fibrillation can come and go, so it might not be present at a doctor’s appointment. This AI algorithm identifies patients who might have atrial fibrillation even when it is not present during their echocardiogram study.”

Related Links:
Cedars-Sinai

Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
12-Channel PC-Based EKG
Avante Velocity EKG

Print article

Channels

Surgical Techniques

view channel
Image: Ureteral electrothermal injury is visible via histology ex vivo (Photo courtesy of Long et al., doi 10.1117/1.BIOS.1.1.015001)

Minimally Invasive Imaging Technique to Revolutionize Ureteral Injury Detection

Electrothermal ureteral injuries are a frequent complication during pelvic surgery. The ureters, which are delicate tubes carrying urine from the kidneys to the bladder, are especially at risk due to their... Read more

Patient Care

view channel
Image: The portable, handheld BeamClean technology inactivates pathogens on commonly touched surfaces in seconds (Photo courtesy of Freestyle Partners)

First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds

Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The Quantra Hemostasis System has received US FDA special 510(k) clearance for use with its Quantra QStat Cartridge (Photo courtesy of HemoSonics)

Critical Bleeding Management System to Help Hospitals Further Standardize Viscoelastic Testing

Surgical procedures are often accompanied by significant blood loss and the subsequent high likelihood of the need for allogeneic blood transfusions. These transfusions, while critical, are linked to various... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.