Sunday, August 10, 2025
Science
No Result
View All Result
  • Login
  • HOME
  • SCIENCE NEWS
  • CONTACT US
  • HOME
  • SCIENCE NEWS
  • CONTACT US
No Result
View All Result
Scienmag
No Result
View All Result
Home Science News Technology and Engineering

AI Algorithm Accelerates Diagnosis and Enhances Care for High-Risk Heart Patients

April 22, 2025
in Technology and Engineering
Reading Time: 4 mins read
0
Prospective Validation of Calibrated Probability Scores and Impact of Probability Score Sorting
65
SHARES
595
VIEWS
Share on FacebookShare on Twitter
ADVERTISEMENT

Mount Sinai researchers have made significant strides in the identification and risk assessment of hypertrophic cardiomyopathy (HCM) through the calibration of an artificial intelligence (AI) algorithm. This innovative approach aims to enhance the accuracy of HCM detection, enabling healthcare professionals to provide timely and individualized care to those affected by this serious heart condition. The study, recently published in the journal NEJM AI on April 22, 2025, highlights the algorithm named Viz HCM, which had already received approval from the Food and Drug Administration for detecting HCM through electrocardiogram (ECG) readings.

The key advancement introduced in this study is the assignment of calibrated numeric probabilities to the algorithm’s assessments. Previously, the algorithm’s output included broad categorizations like “suspected HCM” or “high risk of HCM.” However, researchers at Mount Sinai have refined the process to give patients more concrete information—such as a specific likelihood of having HCM, which could, for example, indicate a 60% chance of the condition. Joshua Lampert, MD, who is the Director of Machine Learning at Mount Sinai Fuster Heart Hospital, emphasized the transformative potential of this detailed feedback for patients. It allows individuals who might not have been previously diagnosed with HCM to gain insights into their heart health, thereby facilitating early intervention and treatment.

The importance of such advancements cannot be overstated, especially considering that HCM affects approximately one in 200 people worldwide and remains one of the leading causes for heart transplantation. Many individuals with HCM are unaware they have the condition until symptoms arise, often when the disease has already progressed to a more severe stage. By integrating this AI tool into clinical workflows, doctors can identify high-risk individuals earlier, potentially preventing critical complications associated with HCM, such as sudden cardiac death—particularly affecting younger patients, who are often in the prime of their lives.

ADVERTISEMENT

The research team under Lampert analyzed nearly 71,000 ECG readings collected from patients between March 2023 and January 2024. Out of these, the Viz HCM algorithm flagged 1,522 cases as showing potential signs of HCM. To validate the findings, researchers conducted an extensive review of patient records and imaging data to establish confirmed diagnoses of HCM. The results yielded promising conclusions: the calibrated AI model effectively provided an accurate correlation between its predicted probabilities of HCM and the actual incidence of the disease among patients.

Enhancing the interpretability of AI in healthcare has become a major focus in recent years, and this study serves as a prime example of how technology can be integrated into clinical practices to improve patient care. Clinicians can leverage this calibrated risk model to prioritize patients according to their individual levels of risk, ultimately streamlining clinical workflows. This change allows healthcare providers to offer more tailored guidance during consultations, transforming how patients experience the healthcare system.

Dr. Vivek Reddy, co-senior author and Director of Cardiac Arrhythmia Services for Mount Sinai Health System, remarked on the transformative potential of these developments in clinical practice. He noted that the utilization of novel algorithms like Viz HCM could significantly enhance patient triage and risk stratification processes. This methodological sophistication underscores the increasing importance of employing advanced AI tools not just for their performance but for their capacity to improve patient outcomes and align with existing clinical practices.

In addition to enhancing patient care through a clearer understanding of individual risks, the research also emphasizes the importance of pragmatic implementation in healthcare settings. Dr. Girish N. Nadkarni, another co-senior author and Chair of the Windreich Department of Artificial Intelligence and Human Health, highlighted that successful integration of AI into medical workflows hinges on its ability to support clinical decision-making while ensuring it aligns with how healthcare is delivered. This study exemplifies a responsible approach to the integration of AI, showcasing that a calibrated model can significantly aid clinicians in managing their patient populations more effectively.

Despite the promising outcomes of this study, the research team acknowledges that further exploration is required for the broader application of this AI calibration strategy across different health systems nationwide. The next phase of research will focus on expanding the use of the calibrated model to ensure its efficacy and adaptability across diverse clinical environments. The ultimate goal is to establish a standardized method for employing AI technology and machine learning algorithms to enhance the predictability and reliability of cardiac diagnoses.

The potential implications of this study extend beyond the realm of HCM, as they pave the way for implementing AI in addressing a wide variety of conditions. As advancements continue within the AI space, cardiologists and healthcare providers are encouraged to remain vigilant and informed about the technological innovations that can be utilized to enhance patient care.

As the healthcare landscape navigates the integration of innovative AI tools, it becomes increasingly critical for medical practitioners to embrace these changes for the benefit of their patients. With the ability to provide targeted risk assessments and improved clinical workflow efficiency, the use of calibrated AI models represents a significant leap forward in the medical field. Researchers and clinicians are hopeful that this will establish a new paradigm in cardiology and beyond, effectively revolutionizing how patients are diagnosed, treated, and monitored.

The Mount Sinai Health System, renowned for its commitment to exceptional cardiovascular care, holds a pivotal role in championing such initiatives. As collaborative efforts among researchers, healthcare professionals, and technology developers continue to flourish, the mounting evidence supporting the role of AI in enhancing accuracy, efficiency, and patient engagement in healthcare will undoubtedly continue to grow.

Subject of Research: Hypertrophic cardiomyopathy (HCM) detection using AI.
Article Title: Calibration of ECG-Based Deep Learning Algorithm Scores for Patients Flagged as High Risk for Hypertrophic Cardiomyopathy.
News Publication Date: April 22, 2025.
Web References: https://www.mountsinai.org/
References: NEJM AI, 2025.
Image Credits: Reproduced with permission from NEJM AI, Lampert, 2025. Copyright 2025 Massachusetts Medical Society.

Keywords

Artificial intelligence, Hospitals, Human health, Heart disease, Risk factors, Machine tools, Cardiology, Cardiomyopathy, Electrocardiography.

Tags: advancements in cardiac care technologyAI algorithm for heart disease diagnosiscalibrated numeric probabilities in healthcareECG-based heart condition assessmentenhancing patient outcomes with AIFDA-approved heart diagnosis technologyhypertrophic cardiomyopathy detectionimproving HCM risk assessmentinnovative healthcare solutions for heart patientsmachine learning in cardiologyMount Sinai research advancementspersonalized care for high-risk patients
Share26Tweet16
Previous Post

Journalist David Zweig Explores American Schools, the Pandemic, and a Tale of Poor Choices

Next Post

Telemedicine Cuts Carbon Emissions by Amount Equal to Eliminating 130,000 Monthly Car Trips in 2023

Related Posts

blank
Technology and Engineering

Enhancing Lithium Storage in Zn3Mo2O9 with Carbon Coating

August 10, 2025
blank
Technology and Engineering

Corticosterone and 17OH Progesterone in Preterm Infants

August 10, 2025
blank
Technology and Engineering

Bayesian Analysis Reveals Exercise Benefits Executive Function in ADHD

August 9, 2025
blank
Technology and Engineering

Emergency Transport’s Effect on Pediatric Cardiac Arrest

August 9, 2025
blank
Technology and Engineering

Bioinformatics Uncovers Biomarkers for Childhood Lupus Nephritis

August 9, 2025
blank
Technology and Engineering

Cross-Vendor Diagnostic Imaging Revolutionized by Federated Learning

August 9, 2025
Next Post
blank

Telemedicine Cuts Carbon Emissions by Amount Equal to Eliminating 130,000 Monthly Car Trips in 2023

  • Mothers who receive childcare support from maternal grandparents show more parental warmth, finds NTU Singapore study

    Mothers who receive childcare support from maternal grandparents show more parental warmth, finds NTU Singapore study

    27531 shares
    Share 11009 Tweet 6881
  • University of Seville Breaks 120-Year-Old Mystery, Revises a Key Einstein Concept

    944 shares
    Share 378 Tweet 236
  • Bee body mass, pathogens and local climate influence heat tolerance

    641 shares
    Share 256 Tweet 160
  • Researchers record first-ever images and data of a shark experiencing a boat strike

    507 shares
    Share 203 Tweet 127
  • Warm seawater speeding up melting of ‘Doomsday Glacier,’ scientists warn

    310 shares
    Share 124 Tweet 78
Science

Embark on a thrilling journey of discovery with Scienmag.com—your ultimate source for cutting-edge breakthroughs. Immerse yourself in a world where curiosity knows no limits and tomorrow’s possibilities become today’s reality!

RECENT NEWS

  • Next-Gen Gravitational-Wave Detectors: Advanced Quantum Techniques
  • Neutron Star Mass Tied to Nuclear Matter, GW190814, J0740+6620

  • Detecting Gravitational Waves: Ground and Space Interferometry
  • Charged Black Holes: Gravitational Power Unveiled.

Categories

  • Agriculture
  • Anthropology
  • Archaeology
  • Athmospheric
  • Biology
  • Bussines
  • Cancer
  • Chemistry
  • Climate
  • Earth Science
  • Marine
  • Mathematics
  • Medicine
  • Pediatry
  • Policy
  • Psychology & Psychiatry
  • Science Education
  • Social Science
  • Space
  • Technology and Engineering

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 4,860 other subscribers

© 2025 Scienmag - Science Magazine

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • HOME
  • SCIENCE NEWS
  • CONTACT US

© 2025 Scienmag - Science Magazine

Discover more from Science

Subscribe now to keep reading and get access to the full archive.

Continue reading