Speciality Medical Dialogues
    • facebook
    • twitter
    Login Register
    • facebook
    • twitter
    Login Register
    • Medical Dialogues
    • Education Dialogues
    • Business Dialogues
    • Medical Jobs
    • Medical Matrimony
    • MD Brand Connect
    Speciality Medical Dialogues
    • Editorial
    • News
        • Anesthesiology
        • Cancer
        • Cardiac Sciences
        • Critical Care
        • Dentistry
        • Dermatology
        • Diabetes and Endo
        • Diagnostics
        • ENT
        • Featured Research
        • Gastroenterology
        • Geriatrics
        • Medicine
        • Nephrology
        • Neurosciences
        • Nursing
        • Obs and Gynae
        • Ophthalmology
        • Orthopaedics
        • Paediatrics
        • Parmedics
        • Pharmacy
        • Psychiatry
        • Pulmonology
        • Radiology
        • Surgery
        • Urology
    • Practice Guidelines
        • Anesthesiology Guidelines
        • Cancer Guidelines
        • Cardiac Sciences Guidelines
        • Critical Care Guidelines
        • Dentistry Guidelines
        • Dermatology Guidelines
        • Diabetes and Endo Guidelines
        • Diagnostics Guidelines
        • ENT Guidelines
        • Featured Practice Guidelines
        • Gastroenterology Guidelines
        • Geriatrics Guidelines
        • Medicine Guidelines
        • Nephrology Guidelines
        • Neurosciences Guidelines
        • Obs and Gynae Guidelines
        • Ophthalmology Guidelines
        • Orthopaedics Guidelines
        • Paediatrics Guidelines
        • Psychiatry Guidelines
        • Pulmonology Guidelines
        • Radiology Guidelines
        • Surgery Guidelines
        • Urology Guidelines
    LoginRegister
    Speciality Medical Dialogues
    LoginRegister
    • Home
    • Editorial
    • News
      • Anesthesiology
      • Cancer
      • Cardiac Sciences
      • Critical Care
      • Dentistry
      • Dermatology
      • Diabetes and Endo
      • Diagnostics
      • ENT
      • Featured Research
      • Gastroenterology
      • Geriatrics
      • Medicine
      • Nephrology
      • Neurosciences
      • Nursing
      • Obs and Gynae
      • Ophthalmology
      • Orthopaedics
      • Paediatrics
      • Parmedics
      • Pharmacy
      • Psychiatry
      • Pulmonology
      • Radiology
      • Surgery
      • Urology
    • Practice Guidelines
      • Anesthesiology Guidelines
      • Cancer Guidelines
      • Cardiac Sciences Guidelines
      • Critical Care Guidelines
      • Dentistry Guidelines
      • Dermatology Guidelines
      • Diabetes and Endo Guidelines
      • Diagnostics Guidelines
      • ENT Guidelines
      • Featured Practice Guidelines
      • Gastroenterology Guidelines
      • Geriatrics Guidelines
      • Medicine Guidelines
      • Nephrology Guidelines
      • Neurosciences Guidelines
      • Obs and Gynae Guidelines
      • Ophthalmology Guidelines
      • Orthopaedics Guidelines
      • Paediatrics Guidelines
      • Psychiatry Guidelines
      • Pulmonology Guidelines
      • Radiology Guidelines
      • Surgery Guidelines
      • Urology Guidelines
    • Home
    • News
    • Cardiac Sciences
    • ECG with AI may...

    ECG with AI may predict AF and death risk in next one year, find studies

    Written by Dr. Kamal Kant Kohli Kohli Published On 2019-11-14T19:20:36+05:30  |  Updated On 14 Nov 2019 7:20 PM IST
    ECG with AI may predict AF and death risk in next one year, find studies

    Advances in computational power paired with massive amounts of data generated in healthcare systems may help solve many clinical problems by application of Artificial intelligence. Time is not far when a patient may see a computer before consulting a doctor.


    The latest addition is to predict future events from an ECG like identifying and predicting the risk of rhythm disorder and mortality in patients. The artificial intelligence (AI) model has been found to identify patients with intermittent atrial fibrillation even when performed during normal rhythm using a quick and non-invasive 10-second test, compared to current tests which can take weeks to years.


    According to two preliminary studies to be presented at the American Heart Association’s Scientific Sessions 2019 — November 16-18 in Philadelphia, Artificial intelligence can examine electrocardiogram (ECG) results to pinpoint patients at higher risk of developing atrial fibrillation or of dying within the next year.


    Researchers from Geisinger Health System in Pennsylvania studied the results of 1.77 million ECGs and related records from almost 400,000 patients. They used more than 2 million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey’s Geisinger Health System to train deep neural networks — advanced, multi-layered computational structures. Both studies, from the same group of researchers, are among the first to use artificial intelligence to predict future events from an ECG rather than to detect current health problems, the scientists noted.


    “This is exciting and provides more evidence that we are on the verge of a revolution in medicine where computers will be working alongside physicians to improve patient care,” said Brandon Fornwalt, M.D., Ph.D., senior author on both studies and associate professor and chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania.


    Researchers speculated that a deep learning model could predict atrial fibrillation (AF) before it develops. Atrial fibrillation is associated with a higher risk of stroke and heart attack. Focusing on 1.1 million ECGs that did not indicate the presence of AF in more than 237,000 patients, researchers used highly specialized computational hardware to train a deep neural network to analyze 15 segments of data — 30,000 data points — for each ECG.


    The researchers found that within the top 1% of high-risk patients, as predicted by the neural network, 1 out of every 3 people was diagnosed with AF within a year. The model predictions also demonstrated longer-term prognostic significance as the patients predicted to develop AF at 1-year had a 45% higher hazard rate in developing AF over 25-year follow-up than the other patients.


    “Currently, there are limited methods to identify which patients will develop AF within the next year, which is why, many times, the first sign of AF is a stroke,” said senior author Christopher Haggerty, Ph.D., assistant professor in the Department of Imaging Science and Innovation at Geisinger. “We hope that this model can be used to identify patients with atrial fibrillation very early so they can be treated to prevent stroke.”


    Jennifer Hall, Ph.D., the American Heart Association Chief of the Institute for Precision Cardiovascular Medicine, noted deep learning is “terrific as another way for us in our field of cardiovascular medicine to be able to help patients and help those understand the risk of stroke.”


    “Being able to understand who is at risk for having irregular heartbeats or atrial fibrillation then helps us understand who may be at risk of also having a stroke and then treating these individuals and preventing both atrial fibrillation and perhaps a stroke down the road,” Hall said. “Having these techniques at our fingertips and having more precise techniques to uncover potential atrial fibrillation now or in the future, is absolutely tremendous.”


    To help identify patients most likely to die of any cause within a year, Geisinger researchers analyzed the results of 1.77 million ECGs and other records from almost 400,000 patients. The team used this data to compare machine learning-based models that either directly analyzed the raw ECG signals or relied on aggregated human-derived measures (standard ECG features typically recorded by a cardiologist) and commonly diagnosed disease patterns.


    The neural network model that directly analyzed the ECG signals was found to be superior for predicting 1-year risk of death. Surprisingly, the neural network was able to accurately predict the risk of death even in patients deemed by a physician to have a normal ECG. Three cardiologists separately reviewed the ECGs that had first been read as normal, and they were generally unable to recognize the risk patterns that the neural network detected, researchers said. “This is the most important finding of this study,” said Fornwalt, who co-directs Geisinger’s Cardiac Imaging Technology Lab with Haggerty. “This could completely alter the way we interpret ECGs in the future.”


    While the vast Geisinger database is a key strength of both studies, the findings should be tested at sites outside of Geisinger, the researchers noted. “Incorporating these models into routine ECG analysis would be simple. However, developing appropriate care plans for patients based on computer predictions would be a bigger challenge,” said lead author Sushravya Raghunath, PhD. Researchers are now testing whether the predictions can be used to improve health outcomes.


    For further reference log on to :

    American Heart Association’s Scientific Sessions 2019
    American Heart AssociationArtificial  intelligenceatrial fibrillationCardiologyelectrocardiogramMortality Risk
    Source : American Heart Association�s Scientific Sessions 2019

    Disclaimer: This site is primarily intended for healthcare professionals. Any content/information on this website does not replace the advice of medical and/or health professionals and should not be construed as medical/diagnostic advice/endorsement or prescription. Use of this site is subject to our terms of use, privacy policy, advertisement policy. © 2020 Minerva Medical Treatment Pvt Ltd

    Dr. Kamal Kant Kohli Kohli
    Dr. Kamal Kant Kohli Kohli
      Show Full Article
      Next Story
      Similar Posts
      NO DATA FOUND

      • Email: info@medicaldialogues.in
      • Phone: 011 - 4372 0751

      Website Last Updated On : 12 Oct 2022 7:06 AM GMT
      Company
      • About Us
      • Contact Us
      • Our Team
      • Reach our Editor
      • Feedback
      • Submit Article
      Ads & Legal
      • Advertise
      • Advertise Policy
      • Terms and Conditions
      • Privacy Policy
      • Editorial Policy
      • Comments Policy
      • Disclamier
      Medical Dialogues is health news portal designed to update medical and healthcare professionals but does not limit/block other interested parties from accessing our general health content. The health content on Medical Dialogues and its subdomains is created and/or edited by our expert team, that includes doctors, healthcare researchers and scientific writers, who review all medical information to keep them in line with the latest evidence-based medical information and accepted health guidelines by established medical organisations of the world.

      Any content/information on this website does not replace the advice of medical and/or health professionals and should not be construed as medical/diagnostic advice/endorsement or prescription.Use of this site is subject to our terms of use, privacy policy, advertisement policy. You can check out disclaimers here. © 2025 Minerva Medical Treatment Pvt Ltd

      © 2025 - Medical Dialogues. All Rights Reserved.
      Powered By: Hocalwire
      X
      We use cookies for analytics, advertising and to improve our site. You agree to our use of cookies by continuing to use our site. To know more, see our Cookie Policy and Cookie Settings.Ok