Four New Studies Demonstrate that Viz.ai Finds New Patients with Hypertrophic Cardiomyopathy Earlier When Embedded into the Clinical Workflow

Rhea-AI Summary

Viz.ai announced new clinical data showing the effectiveness of its Viz HCM module, developed in partnership with Bristol Myers Squibb (NYSE:BMY), in detecting hypertrophic cardiomyopathy (HCM). The module, which received FDA De Novo approval in August 2023, is the first AI algorithm cleared for HCM detection.

Four studies presented at ACC 2025 demonstrated significant results:

• Cleveland Clinic study identified 574 HCM patients with high accuracy
• 20% of patients could have been diagnosed more than one year earlier
• In a multicenter study of 145,848 screened patients, 3% were flagged for suspected HCM, leading to 17 new HCM diagnoses
• Algorithm showed 56% sensitivity and 100% specificity in detecting HCM confirmed by cardiac MRI

Positive

• First FDA-cleared AI algorithm for HCM detection, creating new regulatory category
• Partnership with major pharmaceutical company BMY enhances market position
• 100% specificity and positive predictive value in MRI-confirmed cases
• Demonstrated ability to detect HCM years before traditional diagnosis

Negative

• Relatively low sensitivity rate of 56% in MRI-confirmed cases
• Only 17 new HCM diagnoses from large patient pool of 145,848 screened

Insights

Pharmaceutical Industry Analyst

Bristol Myers Squibb's multi-year partnership with Viz.ai is yielding significant clinical validation for their jointly developed AI technology for hypertrophic cardiomyopathy (HCM). The four studies being presented at ACC 2025 provide compelling evidence that the Viz HCM module effectively identifies patients with this commonly underdiagnosed condition much earlier in the disease course.

What's particularly notable is that this represents the first FDA-cleared AI algorithm for HCM, creating an entirely new regulatory category for cardiovascular machine learning software. This regulatory milestone establishes BMY as an innovator in the AI-enhanced diagnostics space for cardiovascular conditions.

The clinical data demonstrates impressive potential - one study showed that 20% of patients could have been diagnosed more than a year earlier, with some potentially identified up to 10 years before their actual diagnosis. This early detection capability addresses a critical gap in HCM management, where delayed diagnosis frequently leads to suboptimal outcomes.

For BMY, this partnership strategically positions them at the intersection of pharmaceutical development and AI-enabled precision medicine. By helping identify patients earlier, this technology creates a pathway for timely intervention with appropriate therapies, potentially expanding the treatable patient population for BMY's cardiovascular portfolio.

Real-world evidence to be presented at the American College of Cardiology Scientific Session 2025 demonstrates positive impact of Viz.ai on disease detection and care coordination

SAN FRANCISCO--(BUSINESS WIRE)-- Viz.ai, 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 Viz.ai 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 Viz.ai 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.

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 Viz.ai, 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 Viz.ai. “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 Viz.ai, visit us at ACC at booth 11055.

About Viz.ai, Inc.

Viz.ai 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 Viz.ai 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, Viz.ai One delivers significant value to patients, providers, and pharmaceutical and medical device companies. For more information visit Viz.ai.

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Media Contacts

Carolyn Jones

carolyn.jones@viz.ai

Daniel Yunger

daniel.yunger@kekstcnc.com

Source: Viz.ai

FAQ

What are the key findings of Viz.ai's HCM detection studies presented at ACC 2025?

The studies showed that Viz HCM successfully identified 574 HCM patients at Cleveland Clinic, with potential for earlier diagnosis in 20% of cases by more than one year. The system demonstrated 100% specificity in MRI-confirmed cases.

How effective is Viz.ai's AI algorithm in detecting HCM through ECG analysis?

The algorithm showed 56% sensitivity, 100% specificity, and 100% positive predictive value in identifying HCM confirmed by cardiac MRI.

What is the significance of BMY's partnership with Viz.ai for HCM detection?

The partnership resulted in the first FDA-cleared AI algorithm for HCM detection, receiving De Novo approval in August 2023.

How many new HCM patients were identified in Viz.ai's multicenter study?

Out of 145,848 screened patients, the study identified 17 new HCM patients, including 8 inpatient and 9 outpatient diagnoses.

What is the demographic reach of Viz.ai's HCM detection system?

The system demonstrated effectiveness across diverse populations, with 23% Black, 9.2% Asian, and 12.4% Hispanic or Latino patients in the multicenter study.