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




AI Poised to Transform Outcomes in Cardiovascular Health Care

By HospiMedica International staff writers
Posted on 18 Jul 2022
Print article
Image: A new collaboration will advance cardiac health through AI (Photo courtesy of Unsplash)
Image: A new collaboration will advance cardiac health through AI (Photo courtesy of Unsplash)

Employing artificial intelligence (AI) to help improve outcomes for people with cardiovascular disease is the focus of a three-year, USD 15 million collaboration between world-leading experts in machine learning and AI, and outstanding cardiologists and clinicians. The Cardiovascular AI Initiative aims to improve heart failure treatment, as well as predict and prevent heart failure.

Researchers from Cornell University (Ithaca, NY, USA) along with physicians from NewYork-Presbyterian (New York, NY, USA) will use AI and machine learning to examine data from NewYork-Presbyterian in an effort to detect patterns that will help physicians predict who will develop heart failure, inform care decisions and tailor treatments for their patients. The Cardiovascular AI Initiative will develop advanced machine-learning techniques to learn and discover interactions across a broad range of cardiac signals, with the goal of providing improved recognition accuracy of heart failure and extend the state of care beyond current, codified and clinical decision-making rules. It will also use AI techniques to analyze raw data from time series (EKG) and imaging data.

Researchers and clinicians anticipate the data will help answer questions around heart failure prediction, diagnosis, prognosis, risk and treatment, and guide physicians as they make decisions related to heart transplants and left ventricular assist devices (pumps for patients who have reached end-stage heart failure). Future research will tackle the important task of heart failure and disease prediction, to facilitate earlier intervention for those most likely to experience heart failure, and preempt progression and damaging events. Ultimately this would also include informing the specific therapeutic decisions most likely to work for individuals.

“AI is poised to fundamentally transform outcomes in cardiovascular health care by providing doctors with better models for diagnosis and risk prediction in heart disease,” said Kavita Bala, professor of computer science and dean of Cornell Bowers CIS. “This unique collaboration between Cornell’s world-leading experts in machine learning and AI and outstanding cardiologists and clinicians from NewYork-Presbyterian, Weill Cornell Medicine and Columbia will drive this next wave of innovation for long-lasting impact on cardiovascular health care.”

“Artificial intelligence and technology are changing our society and the way we practice medicine,” said Dr. Nir Uriel, director of advanced heart failure and cardiac transplantation at NewYork-Presbyterian, an adjunct professor of medicine in the Greenberg Division of Cardiology at Weill Cornell Medicine and a professor of medicine in the Division of Cardiology at Columbia University Vagelos College of Physicians and Surgeons. “We look forward to building a bridge into the future of medicine, and using advanced technology to provide tools to enhance care for our heart failure patients.”

“Major algorithmic advances are needed to derive precise and reliable clinical insights from complex medical data,” said Deborah Estrin, the Robert V. Tishman ’37 Professor of Computer Science, associate dean for impact at Cornell Tech and a professor of population health science at Weill Cornell Medicine. “We are excited about the opportunity to partner with leading cardiologists to advance the state-of-the-art in caring for heart failure and other challenging cardiovascular conditions.”

Related Links:
Cornell University 
NewYork-Presbyterian 

Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Gold Member
12-Channel ECG
CM1200B
New
5-Drawer Tall Anesthesia Cart
UTGKU-33669-DKB
New
Oxygen Concentrator
ZH-A51

Print article

Channels

Critical Care

view channel
Image: Researchers are working to possibly reduce antibiotic-resistant infections in open bone fractures by employing nanotechnology (Photo courtesy of Zane Lacko/WVU)

Nanotechnology Could Combat Antibiotic-Resistant Infections in Open Bone Fractures

Every year, over 150,000 people in the United States experience open bone fractures. Approximately 10% of these individuals develop infections, which can result in reduced limb function, additional surgeries,... Read more

Surgical Techniques

view channel
Image: A wireless, fully implantable LVAD system could reduce the risk of infections and complications (Photo courtesy of 123RF)

Wireless, Fully Implantable LVAD System to Make Life Easier for Heart Failure Patients

Left Ventricular Assist Devices (LVADs) have traditionally relied on physical drivelines to provide power, creating a connection through the patient's skin. These drivelines increase the risk of infections... 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.