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 hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




Events

27 Jan 2025 - 30 Jan 2025
15 Feb 2025 - 17 Feb 2025

AI Automates Detection of Mitral Regurgitation on Echocardiograms for Minimally Invasive Procedure

By HospiMedica International staff writers
Posted on 23 Aug 2024
Print article
Image: Researchers are using AI to pick up early signs of mitral valve regurgitation, the most common heart valve disorder (Photo courtesy of 123RF)
Image: Researchers are using AI to pick up early signs of mitral valve regurgitation, the most common heart valve disorder (Photo courtesy of 123RF)

Mitral valve regurgitation is the most common but often missed heart valve disorder. Out of the four valves in the heart for facilitating blood movement throughout the body, the mitral valve, situated on the heart's left side, fails to close properly in some individuals, leading to blood flowing backward, a condition known as mitral valve regurgitation. This issue hampers blood circulation and may evolve into more severe complications such as shortness of breath, arrhythmia, and heart failure. Accurately determining the severity of this condition is crucial to deciding whether patients might adopt a watch-and-wait strategy or require immediate intervention. Researchers have now developed an artificial intelligence (AI) program to detect the presence and severity of mitral valve regurgitation from echocardiograms that could help identify patients for a minimally invasive procedure or surgery.

To develop the new program, investigators at Smidt Heart Institute at Cedars-Sinai (Los Angeles, CA, USA) utilized over 58,000 transthoracic echocardiograms—a type of ultrasound imaging used to evaluate heart conditions, including mitral regurgitation. The effectiveness of this program was evaluated using echocardiograms from 1,800 patients at Cedars-Sinai and an additional 915 from Stanford Healthcare. The findings, published in Circulation, show that the AI model demonstrated high accuracy in identifying moderate to severe cases of mitral valve regurgitation. After analyzing videos from more than 50,000 echocardiogram studies, the deep learning model effectively identified the most relevant and important videos for evaluating the severity of mitral regurgitation.

“This could improve how we identify patients with mitral regurgitation, which is becoming more prevalent in our aging population, and to personalize treatment even more so than we already do,” said Raj Makkar, MD, associate director of the Smidt Heart Institute, where the team has also performed more than 1,500 robotic mitral valve repairs with a near 100% success rates.

Related Links:
Smidt Heart Institute

Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
New
Vertebral Body Replacement System
Hydrolift
New
Cannulating Sphincterotome
TRUEtome

Print article

Channels

Surgical Techniques

view channel
Image: The surgical team and the Edge Multi-Port Endoscopic Surgical Robot MP1000 surgical system (Photo courtesy of Wei Zhang)

Endoscopic Surgical System Enables Remote Robot-Assisted Laparoscopic Hysterectomy

Telemedicine enables patients in remote areas to access consultations and treatments, overcoming challenges related to the uneven distribution and availability of medical resources. However, the execution... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... 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 acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.