Ultromics Study Shows AI Could Improve Early Detection of Cardiac Amyloidosis

• Earlier and more precise detection of cardiac amyloidosis is critical: once considered rare and overlooked, it is now moving into the mainstream of cardiology and public awareness, with new drugs, national campaigns and data  demonstrating survival benefits that are enhanced when patients are identified earlier in the disease course.

• Built on one of the world's largest echocardiography datasets and trained on real patient outcomes, Ultromics' AI makes routine heart ultrasounds more powerful by revealing disease patterns invisible to the human eye

• In a multi-center study of 4,815 patients, modelling of Ultromics' EchoGo® Amyloidosis  showed that AI could improve cardiac amyloidosis detection by raising correct referral rates from ~77% to ~80% in low prevalence scenarios and reduced unnecessary referrals in higher prevalence settings

OXFORD, England, Sept. 23, 2025 /PRNewswire/ -- Ultromics, a pioneer in AI-driven cardiology solutions, today announced findings from its new study on artificial intelligence (AI) in echocardiography, presented at the American Society of Echocardiography's (ASE) 2025 Scientific Sessions in Nashville, Tennessee. Published as an abstract in the Journal of the American Society of Echocardiography (JASE), the study points to the growing role of AI in helping doctors find cardiac amyloidosis sooner.

Once diagnosed only after years of unexplained heart failure symptoms, cardiac amyloidosis is now at the center of cardiology. Ads for new drugs are running on primetime TV, specialists are filling conference halls, and with AI able to spot the disease on routine heart ultrasounds,   this could support earlier intervention in the disease course, when treatment may offer greater benefit.

Drawing on 4,815 patient cases from 17 hospitals in the United States and United Kingdom, Ultromics modelled how EchoGo^® Amyloidosis could improve referral decisions in real-world practice. The AI was able to detect cardiac amyloidosis earlier and more accurately than traditional methods, finding patients who would otherwise have been missed while reducing unnecessary testing. The results held true across both low- and high-prevalence settings, showing the potential impact of AI in everyday clinical practice. Major findings included:

• In low-prevalence scenarios, referral decisions based on wall thickness alone correctly identified ~65% of patients with cardiac amyloidosis. Incorporating AI increased correct referral rates to ~76–80%, meaning more patients could be identified earlier while avoiding unnecessary referrals.^[1]
• In higher-prevalence scenarios, AI could reduce unnecessary referrals by up to 18% while maintaining high detection rates.^[1]
• The findings were consistent across hospitals in both the United States and United Kingdom, underscoring the technology's potential for broad clinical use.^[1]

Cardiac amyloidosis is increasingly recognized as a common driver of heart failure. Newly available therapies such as tafamidis and acoramidis can slow disease progression and reduce mortality, but they are effective only when patients are identified early. Unfortunately, up to 66% of cases go undiagnosed in clinical practice.^[2-4]

"Too often, patients with cardiac amyloidosis are diagnosed only after years of unexplained symptoms and irreversible damage," said Dr. Ashley Akerman, Director of Clinical Sciences at Ultromics and lead author of the study. Our findings suggest that using EchoGo^® Amyloidosis to  enhance routine heart scans, doctors could better identify at-risk patients, reduce unnecessary testing, and ensure that those who need confirmatory diagnosis and treatment, receive it sooner."

EchoGo^® Amyloidosis is designed to help close this diagnostic gap by analyzing echocardiograms at the pixel level to detect subtle patterns often missed by the human eye. Trained and validated on 7,174 patients (9,700+ echo videos) from 15 international sites, and tested on more than 2,700 additional patients across 18 sites, the model achieved high accuracy (AUC 0.93) across multi-ethnic, real-world populations. Its cardiac amyloidosis model provides consistent, automated assessments that help clinicians identify at-risk patients sooner, improve referral decisions for confirmatory testing and connect more patients to life-prolonging therapies.^[5]

This study adds to the growing clinical validation of Ultromics' EchoGo^® platform, the first FDA-cleared and Medicare-reimbursed AI system for echocardiography. With results documented in more than 25 peer-reviewed studies, EchoGo^® is already in use at leading U.S. hospitals including UChicago Medicine, Northwestern, and City of Hope, where it supports earlier detection of complex cardiovascular conditions and more precise patient management.

About Ultromics
Founded out of the University of Oxford, Ultromics is redefining cardiovascular care with FDA-cleared, AI-powered tools that enhance echocardiographic diagnosis. Built in partnership with the NHS and Mayo Clinic, its EchoGo^® platform helps clinicians detect complex heart diseases earlier and more accurately—using nothing more than a standard ultrasound scan. Ultromics is backed by leading investors and U.S. healthcare systems and is on a mission to transform how heart disease is diagnosed and treated. For more, visit www.ultromics.com.

Reference:

¹Akerman AP, et al. J Am Soc Echocardiogr. 2025;38(9S):Axxx.
²González-López E, et al. Eur Heart J. 2015;36:2585–94.
³Hahn VS, et al. JACC Heart Fail. 2020;8:712–24.
⁴AbouEzzeddine OF, et al. JAMA Cardiol. 2021;6:1267–74.
⁵Slivnick JA, Hawkes W, et al. Eur Heart J. 2025;ehaf387.

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SOURCE Ultromics