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Simultaneous publication in Journal of the American College of Cardiology demonstrates impact of ECG-AI on heart failure risk assessment<\/p>\n
CAMBRIDGE, Mass., November 09, 2025–(BUSINESS WIRE<\/a>)–Anumana<\/a>, 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<\/a>, 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.<\/p>\n 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\u2014the Framingham Heart Study<\/a>, Multi-Ethnic Study of Atherosclerosis<\/a>, and Cardiovascular Health Study<\/a>. Investigators found that integrating Anumana\u2019s 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.<\/p>\n “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\u2019s 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.”<\/p>\n The study was conducted with the National Heart, Lung, and Blood Institute\u2019s HeartShare\/AMP Heart Failure Program<\/a> using the BioData Catalyst platform<\/a>, 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.<\/p>\n Story Continues <\/p>\n “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.”<\/p>\n Anumana\u2019s ECG-AI\u2122 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.<\/p>\n In addition to the featured science, Anumana presented three abstracts highlighting the versatility of AI across cardiovascular conditions:<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n Together, these studies reinforce Anumana\u2019s leadership in translating advanced AI models into clinically meaningful solutions that support earlier detection, targeted intervention, and improved cardiovascular outcomes.<\/p>\n About Anumana<\/p>\n 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\u2019s 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\u2019s FDA-cleared ECG-AI\u2122 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<\/a>. Please visit www.anumana.ai<\/a> for more information and follow Anumana on LinkedIn<\/a> and X<\/a> for company updates.<\/p>\n View source version on businesswire.com: https:\/\/www.businesswire.com\/news\/home\/20251109777744\/en\/<\/a><\/p>\n Contacts<\/p>\n