Using artificial intelligence to build solutions that help answer the greatest unmet need in cardiovascular medicine.
New published researchJACC Advances: Automated Detection of HFpEF using AI
HFpEF - a complex form of heart failure - is increasingly prevalent and often goes undiagnosed, progressing silently until patients need emergency care. When the disease takes hold, the prognosis is grim. Patients experience poor quality of life, high symptom burden, and face a median life expectancy.
The 5-year mortality rate after hospitalization remains unacceptably high (upwards of 50%).1 That's why Ultromics is committed to catching the disease early.
A new class of heart failure technology
Ultromics brings the power of artificial intelligence into your echocardiography workflow using a state-of-the-art platform that supports early heart failure diagnosis. EchoGo Heart Failure unlocks a new pathway for heart failure care, bringing patients closer to a definitive diagnosis, accelerating earlier diagnosis and treatment, and helping to improve the lives of many.
Compared to current clinical algorithms, EchoGo Heart Failure resulted in an accurate diagnostic output for more patients, and successfully identified patients with worse 5-year survival.4
The beat of change
A first-of-kind solution
Detect HFpEF with precision from a single apical four-chamber echocardiogram view.
Using our FDA-cleared system, our platform has helped clinicians solve indeterminate cases and helped providers reduce costs.
increase in accuracy4
Improved detection of HFpEF with fewer indeterminates4
See our proof and validationSee more
Verified in clinical settings
Our current customers have indicated major improvements in their pain and process while using EchoGo Heart Failure. Our technology is backed by collaborations with industry leading clincians, engineers, and research institutions.
“This novel solution applies AI to cardiovascular imaging to greatly simplify identification of patients with HFpEF, a diagnosis that can be challenging to make, and allow more expeditious treatment.”
Time to focus on HFpEF
Diagnosis is challenging
Currently, diagnosis relies on a combination of symptomatology and echocardiographic evidence and the exclusion of non-cardiac causes of dyspnea according to clinical scores. This complexity is a challenge, and up to 64% of patients are misdiagnosed3, leading to avoidable invasive hemodynamic measurements and missed treatment.
Key issues are preventing progress
Julian, 81, presented symptoms of shortness of breath and oedema, but had undetermined results when his diastolic function was assessed.
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Together with experts
Each of our partners are innovation leaders are working with us to make lives better.
We partner with exceptional leaders across all stages to build transformative technologies.
Some of who we work with
Our impact across heart failure
“The FNIH will harness valuable perspectives and expertise of a select number of collaborations, including Ultromics, to alleviate this unmet need and pave the way for better classification and more precise treatment strategies.”
We are redefining heart failure as we know it
Innovation is at the heart of everything we do
At the heart of our mission is a commitment to making heart failure detection easier for providers and prognosis better for patients. Healthcare professionals should receive critical information they need minimizing the added risks and costs of untreated patients or further procedures. Staying true to the standards set by our Oxford University origins, we are partnering with the most trusted and validated medical organizations in the world to improve quality of life and reduce the burden on the healthcare system at large.
cases processed so far
pixels analyzed by our AI algorithm
of outcome data
- Gerber Y, Weston SA, Redfield MM, et al. A contemporary appraisal of the heart failure epidemic in Olmsted County, Minnesota, 2000 to 2010. JAMA Internal Medicine. 2015;175:996–1004.
- Savarese G and Lund LH. Global public health burden of heart failure. Cardiac Failure Review. 2017;3:1:7-11.
- Borlaug, BA, Sharma K, Shah SJ, et al. Heart failure with preserved ejection fraction, :JACC Scientific Statement. Journal of the American College of Cardiology. 2023;81:1810–1834.
- Akerman AP, Porumb M, Beqiri A, et al. Comparison of clinical algorithms and artificial intelligence applied to an echocardiogram to categorize risk of heart failure with preserved ejection fraction. Journal of the American College of Cardiology. ACC. 2023;81 (8_Supplement) 360.
*based on an example use case.
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