Simultaneous publication in Journal of the American College of Cardiology demonstrates impact of ECG-AI on heart failure risk assessment

Anumana, a leader in AI-powered cardiovascular diagnostics, today announced new groundbreaking clinical data presented at the American Heart Association (AHA) Scientific Sessions 2025. Among the studies highlighted was a late-breaking featured science presentation, published simultaneously in the Journal of the American College of Cardiology, showing that AI applied to the electrocardiogram (ECG-AI) can enhance near-term prediction of incident heart failure beyond established clinical risk models. Anumana also has three additional abstracts being presented at the meeting.

The featured study, “Enhanced Prediction of Incident Heart Failure Using Artificial Intelligence-Driven Analysis of 12-Lead Electrocardiogram Waveforms: A HeartShare/AMP-HF Pooled Cohort Analysis,” analyzed data from more than 14,000 participants across three major longitudinal cohorts—the Framingham Heart Study, Multi-Ethnic Study of Atherosclerosis, and Cardiovascular Health Study. Investigators found that integrating Anumana’s ECG-AI with the PREVENT-HF clinical risk equation significantly improved short-term heart failure risk prediction, reclassifying up to 12.5% of individuals into higher-risk categories not identified by clinical factors alone. Participants with positive ECG-AI results were more than 20 times as likely to develop heart failure within three years as those with negative results.

“AI analysis of standard 12-lead electrocardiograms (ECG-AI) allows detection of subtle electrical changes that signal early cardiac dysfunction. The findings from this analysis suggest that ECG-AI can enhance detection of patients at risk for development of HF when added to standard clinical risk assessment with the PREVENT-HF score,” said Akshay S. Desai, MD, MPH, Director of the Heart Failure Disease Management Program, Brigham and Women’s Hospital, and lead study investigator. “The implication is that ECG-AI may help clinicians to identify at-risk patients years before symptoms of HF appear, creating opportunities to start preventive therapy sooner and improve long-term outcomes.”

The study was conducted with the National Heart, Lung, and Blood Institute’s HeartShare/AMP Heart Failure Program using the BioData Catalyst platform, which accelerates reproducible biomedical research to drive scientific advances. The collaboration enabled access to deeply phenotyped, longitudinal cohorts and robust analytic capabilities, supporting a rigorous evaluation of ECG-AI for predicting early heart failure risk.

“This publication marks meaningful progress toward a future where AI helps prevent disease rather than just detecting it,” said Simos Kedikoglou, MD, President and COO of Anumana. “It demonstrates how our ECG-AI LEF algorithm can uncover early signs of heart failure, supporting clinicians in identifying at-risk patients sooner and enabling more proactive care.”

Anumana’s ECG-AI™ LEF algorithm analyzes standard 12-lead ECGs to identify patients with LEF, a key indicator of heart failure. The algorithm achieved an AUC of 0.944, with a sensitivity of 90.2% and specificity of 85.1%, reflecting its strong ability to detect patients at risk for heart failure.

In addition to the featured science, Anumana presented three abstracts highlighting the versatility of AI across cardiovascular conditions:

• Multicenter Study of ECG-AI for Pulmonary Hypertension: Across five U.S. health systems, ECG-AI detected pulmonary hypertension with 84% sensitivity and 72% specificity, supporting earlier disease identification.
• AI ECG for Early Identification of Pulmonary Arterial and Chronic Thromboembolic Pulmonary Hypertension: This retrospective real-world data analysis found that between initial symptom presentation and diagnosis, more than 74% of patients evaluated had at least one ECG, which was flagged as positive by ECG-AI PH, suggesting potential to reduce diagnostic delays.
• Parity and Takotsubo Cardiomyopathy: Researchers developed a novel methodology to determine the lifetime number of pregnancies from electronic health record data to evaluate whether there is an association between parity and risk of Takotsubo cardiomyopathy.

Together, these studies reinforce Anumana’s leadership in translating advanced AI models into clinically meaningful solutions that support earlier detection, targeted intervention, and improved cardiovascular outcomes.

About Anumana

Anumana is an AI-driven health technology company committed to transforming cardiovascular care. Co-founded by nference and Mayo Clinic, Anumana develops software-as-a-medical-device (SaMD) solutions that apply multimodal AI to support early detection, clinical decision-making, and intraoperative guidance across the continuum of care. The company’s portfolio includes ECG-based algorithms, generative imaging applications, and real-time procedural support tools designed to improve outcomes in both diagnostic and perioperative settings. The company’s FDA-cleared ECG-AI™ LEF algorithm is currently available in the U.S. and eligible for reimbursement as of January 2025. To learn more or schedule a demo, visit ECG-AI LEF. Please visit www.anumana.ai for more information and follow Anumana on LinkedIn and X for company updates.

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