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 Predicts Sudden Cardiac Death and Cardiovascular Risk

By HospiMedica International staff writers
Posted on 09 Nov 2023
Print article
Image: AI could accurately detect heart valve disease and predict cardiovascular risk (Photo courtesy of 123RF)
Image: AI could accurately detect heart valve disease and predict cardiovascular risk (Photo courtesy of 123RF)

Recent breakthroughs in artificial intelligence (AI) have led to promising developments in the healthcare sector, especially in heart health monitoring and risk prediction for cardiovascular diseases. At the American Heart Association’s Scientific Sessions 2023, researchers presented two studies showcasing the potential of AI in these areas. One of the studies demonstrated that an AI system, when analyzing audio data from a digital stethoscope, outperformed healthcare professionals in detecting heart valve disease. These professionals traditionally relied on acoustic cues from a conventional stethoscope. Another study showed the capability of an AI-based deep learning program to assess eye images for evaluating the risk of cardiovascular events in individuals with prediabetes and Type 2 diabetes.

In the first study conducted across three primary care clinics in the U.S., researchers at Vanderbilt University (Nashville, TN, USA) put a traditional practice to the test against AI technology. They compared how well a medical professional using an ordinary stethoscope could identify potential heart valve disease versus an AI system analyzing sounds from a digital stethoscope. Participants underwent a physical examination which included both the traditional method and the digital stethoscope recording. Follow-up echocardiograms confirmed the presence of heart valve disease, although these findings were not disclosed to either the clinician or the patient. The AI system detected valvular heart disease in 94.1% of the cases, a significant increase compared to the 41.2% detection rate by healthcare professionals using the standard stethoscope. The AI also flagged 22 individuals with moderate-to-severe heart valve disease that had not been diagnosed previously, while only eight such cases were caught by the traditional method. However, the human professionals demonstrated higher specificity in their diagnoses (95.5%) compared to the AI system (84.5%), suggesting a reduced likelihood of false positives that could lead to unnecessary additional testing.

In the second study, researchers at Mass General Brigham (Boston, MA, USA) analyzed retina images using a deep-learning algorithm. This method was assessed for its effectiveness in predicting cardiovascular events—like heart attacks, strokes, and related deaths—in over a thousand patients with prediabetes or Type 2 diabetes. Using the deep-learning algorithm, the participants were categorized into low, moderate, and high-risk categories based on the analysis of their retinal images, and their health was tracked over an 11-year period. The researchers found that those in the low-risk group had an 8.2% incidence of cardiovascular events, while those in the moderate and high-risk groups experienced higher incidences, at 15.2% and 18.5%, respectively. Even after considering demographic and other risk factors like age, gender, and lifestyle, those in the moderate-risk category were 57% more likely to suffer a cardiovascular event, and those deemed high-risk were 88% more likely, both compared to the low-risk group.

“Computational methods to develop novel predictors of health and disease — ‘artificial intelligence” — are becoming increasingly sophisticated,” said Dan Roden, M.D., FAHA, professor of medicine, pharmacology and biomedical informatics and senior vice-president for personalized medicine at Vanderbilt University Medical Center, as well as chair of the Association’s Council on Genomic and Precision Medicine. “Both of these studies take a measurement that is easy to understand and easy to acquire and ask what that measurement predicts in the wider world.”

Related Links:
Vanderbilt University 
Mass General Brigham 

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)
New
Phlebotomy Chair
CHE03/BH
New
Shoulder Positioner
HE-JB2

Print article

Channels

Surgical Techniques

view channel
Image: Design and fabrication of biodegradable electrode for brain stimulation (Photo courtesy of Biomaterials, DOI:10.1016/j.biomaterials.2024.122957)

Biodegradable Electrodes Repair Damaged Brain Tissue Without Need for Surgical Removal

Neurological disorders often lead to irreversible cell loss and are a major cause of disability worldwide, with limited treatment options available. A promising therapeutic approach is the stimulation... 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 © 2000-2025 Globetech Media. All rights reserved.