Latest news with #VizHCM
Hans India
26-04-2025
- Health
- Hans India
AI algorithm can help identify high-risk heart patients: Study
New Delhi: A team of US researchers, studying a type of heart disease known as hypertrophic cardiomyopathy (HCM) said they have calibrated an artificial intelligence (AI) algorithm to quickly and more specifically identify patients with the condition and flag them as high risk for greater attention during doctor's appointments. The algorithm, known as Viz HCM, had previously been approved by the Food and Drug Administration (FDA) for the detection of HCM on an electrocardiogram (ECG). The Mount Sinai study, published in the journal NEJM AI, assigns numeric probabilities to the algorithm's findings. For example, while the algorithm might previously have said 'flagged as suspected HCM' or 'high risk of HCM,' the Mount Sinai study allows for interpretations such as, 'You have about a 60 percent chance of having HCM,' said Joshua Lampert, Director of Machine Learning at Mount Sinai Fuster Heart Hospital. As a result, patients who had not previously been diagnosed with HCM may be able to get a better understanding of their individual disease risk, leading to a faster and more individualized evaluation, along with treatment to potentially prevent complications such as sudden cardiac death, especially in young patients. 'This is an important step forward in translating novel deep-learning algorithms into clinical practice by providing clinicians and patients with more meaningful information. Clinicians can improve their clinical workflows by ensuring the highest-risk patients are identified at the top of their clinical work list using a sorting tool,' said Lampert, Assistant Professor of Medicine (Cardiology, and Data-Driven and Digital Medicine) at the Icahn School of Medicine at Mount Sinai. HCM impacts one in 200 people worldwide and is a leading reason for heart transplantation. However, many patients don't know they have the condition until they have symptoms and the disease may already be advanced. 'This study reflects pragmatic implementation science at its best, demonstrating how we can responsibly and thoughtfully integrate advanced AI tools into real-world clinical workflows,' said co-senior author Girish N Nadkarni, Chair of the Windreich Department of Artificial Intelligence and Human Health and Director of the Hasso Plattner Institute for Digital Health.
Associated Press
26-03-2025
- Health
- Associated Press
Four New Studies Demonstrate that Viz.ai Finds New Patients with Hypertrophic Cardiomyopathy Earlier When Embedded into the Clinical Workflow
SAN FRANCISCO--(BUSINESS WIRE)--Mar 26, 2025-- the leader in AI-powered disease detection and intelligent care coordination, today announced new clinical data demonstrating how the Viz HCM module enables faster, accurate detection of signs of hypertrophic cardiomyopathy (HCM) to help ensure that more patients receive the care they need. Four studies, which will be presented at the American College of Cardiology's (ACC) Annual Scientific Session & Expo 2025, show the real-world impact of in clinical practice with earlier detection and triaging of new patients with HCM, a commonly inherited heart disease that often goes undetected. The Viz HCM module, developed as part of a multi-year agreement between and Bristol Myers Squibb (NYSE:BMY), is the first and only AI algorithm cleared by the U.S. Food and Drug Administration (FDA) for HCM. This press release features multimedia. View the full release here: Viz HCM: Patient card displaying key clinical information, including ECG and echocardiographic findings. 'It's exciting to see the growing real-world evidence showing how AI-enhanced ECG analysis can play a pivotal role in identifying new patients with hypertrophic cardiomyopathy,' said Milind Desai, MD, MBA, Director of the Center for Hypertrophic Cardiomyopathy at Cleveland Clinic. 'By leveraging AI as a second set of eyes, we can expand the ability to diagnose more HCM patients earlier and across diverse populations, tackling a condition that's often challenging to detect.' Viz HCM uses artificial intelligence to analyze all 12-lead electrocardiograms (ECGs) at the point of care from across a health system to identify suspected HCM cases, notify cardiology care teams and increase the likelihood that patients get the right follow-up and diagnosis. The Viz HCM module was granted De Novo approval by the FDA in August 2023, creating a new regulatory category for cardiovascular machine learning-based notification software. 'The findings from our study highlight the potential of AI-based ECG analysis to identify hypertrophic cardiomyopathy well before a clinical diagnosis is made,' said Michael Ayers, MD, Co-Director of the HCM Center of Excellence at University of Virginia. 'By detecting HCM months or even years earlier, this technology could allow for earlier intervention, potentially improving patient outcomes and altering the course of the disease.' The following clinical studies are being presented at ACC: 'Real-World Artificial Intelligence–Based Electrocardiographic Analysis to Diagnose Hypertrophic Cardiomyopathy' evaluated the performance of Viz HCM for detecting HCM at the Cleveland Clinic. The study, published in JACC: Clinical Electrophysiology and set to be presented live at ACC 2025, demonstrated that Viz HCM achieved a high degree of accuracy in detecting HCM. The AI-ECG successfully identified 574 HCM patients, and 691 were determined to have an alternate clinically relevant diagnosis, highlighting Viz HCM's value for more effective disease detection. 'A Retrospective Assessment of Delays in HCM Diagnosis and the Potential Impact of an Artificial-Intelligence-assisted Electrocardiogram Screening' used Viz HCM to predict HCM from serial 12-lead ECGs first and after which, the confirmatory diagnosis was assessed by expert clinicians at an HCM Center of Excellence. Results indicate that Viz HCM could have identified HCM patients from an ECG earlier. Among the 155 patients with AI-based ECG identifications of HCM, 20.0% could have been diagnosed more than one year prior, 12.9% more than 3 years prior, 9.0% more than 5 years prior, and 4.5% more than 10 years prior. 'A Multicenter, Prospective Cohort Pilot Study on the Clinical Implementation and Utilization of an AI-based ECG Tool for HCM Detection and Care Coordination' evaluated the implementation of Viz HCM into the clinical workflow to detect HCM and triage patients to the right specialist. Out of 145,848 screened patients, 3% were flagged for suspected HCM and directed to the appropriate specialist. A total of 217 patients met the study criteria and were enrolled, representing a diverse population—23% Black, 9.2% Asian, and 12.4% Hispanic or Latino. Out of the 217 patients, 17 new HCM patients were identified, including 8 inpatient and 9 outpatient diagnoses. The findings suggest that AI-assisted ECG screening can be successfully integrated into clinical workflows to aid in improved HCM identification and care coordination. 'Machine-learning Algorithm for the Detection of Hypertrophic Cardiomyopathy from Standard Electrocardiogram' evaluated the performance of the Viz HCM algorithm in identifying HCM confirmed by cardiac MRI. The study found that Viz HCM identified 87 of 156 patients with HCM, rendering its sensitivity 56%, specificity 100%, and positive predictive value of 100%. 'At we are committed to integrating AI into clinical workflows to ensure the reliable detection and timely triage of underdiagnosed conditions like HCM, ultimately enhancing care and outcomes for more patients,' said Molly Madziva Taitt, Ph.D., VP of Global Clinical Affairs at 'The robust clinical evidence accepted at ACC underscores the strong and consistent performance of the Viz HCM module and as a practical tool for efficiently triaging patients for clinical evaluation with the right specialist at the right time.' To learn more about visit us at ACC at booth 11055. About Inc. is the pioneer in the use of AI algorithms and machine learning to increase the speed of diagnosis and care across 1,700+ hospitals and health systems in the U.S. and Europe. The AI-powered One TM is an intelligent care coordination solution that identifies more patients with a suspected disease, informs critical decisions at the point of care, and optimizes care pathways and helps improve outcomes. Backed by real-world clinical evidence, One delivers significant value to patients, providers, and pharmaceutical and medical device companies. For more information visit Carolyn Jones [email protected] Yunger SOURCE: Copyright Business Wire 2025. PUB: 03/26/2025 09:24 AM/DISC: 03/26/2025 09:25 AM
