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Detect heart failure with precision

As heart failure prevalence increases globally, there is a growing need for new innovative solutions to empower healthcare professionals with precision heart failure detection.

The impact on the healthcare industry is substantial

Rising cases

Heart failure cases are projected to double by 2030, resulting in an estimated $70 billion in annual spending. [2]

Rising cases

Overstretched resources

Heart failure contributes to 5.2M emergency visits per year, increasing clinical, operational, and financial burden.

Overstretched resources

High readmissions

Over 50% of patients hospitalized with heart failure are readmitted within 1 year, with an average unreimbursed cost ranging between $15,000 and $25,000.

High readmissions

Low reimbursement incentive

Many heart failure hospitalizations are reimbursed nearly $10,000 per admission below the actual cost of care.

Low reimbursement incentive

Today’s approaches aren't working

Our traditional approaches to detect heart failure aren’t holding up to the drastic changes in both the healthcare industry and the population. 

While echocardiography is the first-line test for investigation, current clinical algorithms continue to miss diagnosis. In addition, we’re experiencing a drastic shortage of cardiologists. The Association of American Medical Colleges estimates a shortage of up to 139k jobs by 2033.

This leads to a broad disparity in how effectively echocardiography is used to support proper and timely diagnosis. 

We need better workflows and to increase throughput to improve heart failure outcomes

The challenge with diagnosis

>50% have HFpEF

Over half of patients with heart failure have preserved ejection fraction where diastolic dysfunction is missed.

18 months wait time

Patients at many organizations are waiting up to 18 months for an echo exam. 

75% missed

According to literature, current clinical algorithms misdiagnose heart failure up to 75% of the time.

Precision heart failure detection

Let's detect heart failure more effectively

Ultromics combines echocardiography - the most common and cost effective cardiac imaging modality - with AI-derived insights that go beyond human capability.

AI is a critical and necessary tool for providers to meet and overcome economic challenges and improve patient care.

The technology detects disease earlier and more consistently, alleviating the need for more costly and invasive procedures.

Myocardial Strain

Recognized for its value in early disease detection and heightened sensitivity over ejection fraction, guidelines emphasize the importance of adding global longitudinal strain to regular reporting practices and ensuring these measurements can be performed with minimal variability and in a timely manner. [4,5]

Analyzing strain can help you detect heart failure and provide earlier management even before visual changes and symptoms occur.

With EchoGo, adding strain to routine echocardiograms is fast, easy, and accurate.

HFpEF detection

Our AI increases HFpEF detection to 90% accuracy. This advanced technology offers a rapid and an improved means for the detection of early-stage heart failure and may reduce hospitalizations by 30%. [6,7]

We  deliver confidence in HFpEF diagnosis, mitigate the risk of a missed diagnosis, and reduce the need for further invasive testing. Patients can receive treatments earlier, improving quality of life and reducing hospitalization and readmission costs.


Time to focus on HFpEF

Over half of heart failure patients have HFpEF; a leading cause of mortality with higher morbidity and hospitalizations than heart failure with reduced ejection fraction (HFrEF).

Diagnosing HFpEF is complicated and relies on invasive testing or time-consuming and subjective analysis (such as Doppler) that is often inconclusive. 

Rates of misdiagnosis using these clinical algorithms can be up to 75% in some care settings, delaying therapy and contributing to the high number of hospitalizations and poor 5-year mortality rate.



New treatments make it more important than ever to quickly and accurately diagnose HFpEF

Sodium-glucose cotransporter 2 inhibitors
(SGLT2is) and other new drug therapies have been shown to improve both HFrEF and HFpEF patient outcomes. [6]

These SGLT2is can reduce death and hospitalizations by 20-40%. [5]

Effective and fast diagnoses lead to earlier treatment with these new classes of drugs, and combined with effective patient management, can have a significant impact.

Gain new insights. Save more lives.

AI can perform tirelessly in any location and for any socio-economic demographic. It levels the clinical playing field for all providers and their patients. 

With cloud-based and easy-to-read reports, you’ll have a more precise understanding of heart failure that helps you save more lives.


  1. Beqiri A, Parker A, Mumith A et al. Fully automated quantification of contrast and non-contrast echocardiograms completely eliminates inter-operator variability. Ultromics. 2020
  2. Zohrabian A, Kapp JM, Simoes EJ. The economic case for US hospitals to revise their approach to heart failure readmission reduction. Annuals of Translational Medicine. 2018 Aug;6(15):298.
  3. Shah SJ, Borlaug BA, Kitzman DW, et al. Research Priorities for Heart Failure With Preserved Ejection Fraction: National Heart, Lung, and Blood Institute Working Group Summary. Circulation. 2020;141:1001-1026.Smiseth, OA, et al. European Heart Journal. 2015;37:1196–1207
  4. Edvardsen T,  Asch FM, Davidson B, et al. Non-Invasive Imaging in Coronary Syndromes: Recommendations of The European Association of Cardiovascular Imaging and the American Society of Echocardiography, in Collaboration with The American Society of Nuclear Cardiology, Society of Cardiovascular Computed Tomography, and Society for Cardiovascular Magnetic Resonance. Journal of The American Society of Echocardiography. 2022;35:329-354
  5. Cardoso R, Graffunder FP, Ternes CM et al.  SGLT2 inhibitors decrease cardiovascular death and heart failure hospitalizations in patients with heart failure: A systematic review and meta-analysis. EClinicalMedicine.2021;36:100933
  6. FDA Submission Documents

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