Assess Heart Failure Across Every Phenotype
Echocardiography defines the heart failure phenotype, HFrEF, HFmrEF, or HFpEF. Us2.ai measures every guideline-recommended parameter (LVEF, GLS, diastolic function, and more) automatically on every scan, and reports the guideline-based category for the clinician to confirm.
The World's Fastest-Growing Cardiovascular Epidemic
Heart failure affects 1–2% of adults in developed countries and over 10% of those aged 70+. Hospital admissions for HF are projected to increase 50% in the next 25 years.
Heart failure is a global pandemic. With aging populations and increasing prevalence of risk factors, the burden is growing faster than the workforce can keep up.
One in five heart failure patients die within a year of diagnosis. Five-year mortality reaches 53–67%, worse than many cancers.
After an acute heart failure hospitalization, over 45% of patients die or are readmitted within 12 months. Early, accurate diagnosis is the first step to changing this.
Three Phenotypes, One Echo Measurement
International guidelines classify heart failure by LVEF. Echocardiography defines the phenotype and directly determines the treatment pathway.
Guideline Measurements for Every Heart Failure Phenotype
Heart failure assessment hinges on echocardiography. Us2.ai measures every parameter in the diagnostic pathway with the accuracy and reproducibility that distinguishes HFrEF, HFmrEF, and HFpEF.
LVEF Classification
Accurate, reproducible LVEF measurement is the single most important echo parameter in heart failure. It defines the phenotype and determines the treatment pathway. Us2.ai reduces inter-observer variability.
Diastolic Function Assessment
E/e' ratio, LA volume index, and TR velocity are critical for diagnosing HFpEF. Us2.ai measures all diastolic parameters on every study, essential for the most diagnostically challenging HF phenotype.
Global Longitudinal Strain
GLS detects subclinical dysfunction before LVEF drops. International guidelines note GLS <16% as a marker for HFpEF, and a ≥12% relative GLS reduction is superior to LVEF for detecting cardiotoxicity.
Right Heart & Structural Assessment
TAPSE, RV function, LA volume index, LV mass index, and relative wall thickness. Us2.ai delivers the full structural assessment that supports HFmrEF and HFpEF diagnosis.
International guidelines recommend a follow-up 3–6 months after therapy optimization and at any clinical deterioration. Us2.ai’s automated, standardized measurements enable reliable serial comparison, detecting changes that matter and tracking treatment response over time.
Validated by Leading Heart Failure Clinicians
Us2.ai was validated against expert sonographers across four independent international cohorts, with results published in The Lancet Digital Health. Two of the field's most prominent heart failure investigators discuss what the evidence shows.
I was so skeptical. I don't think it's going to work. But I have come around to realizing that not only does it work. We absolutely need this.
If you take multiple cohorts and test against expert human readers, the message again and again is: it's as good if not better than humans. Trust the numbers.
Tromp J, Seekings PJ, Hung C-L, et al. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study
Lancet Digit Health 2022;4(1):e46–e54
A deep-learning workflow validated against expert human readers across four independent international cohorts. The AI classified systolic and diastolic dysfunction as accurately as expert sonographers, with less measurement variability, supporting reproducible, guideline-concordant assessment at scale.
Read the validation studyAI-Assisted Detection of Heart Failure from Electronic Health Records
Published with the University of Dundee, this study demonstrates how AI echocardiography can identify missed heart failure patients from electronic health records.
Oo et al. Artificial Intelligence-Assisted Automated Heart Failure Detection and Classification from Electronic Health Records
ESC Heart Fail 2024;11:2769–2777
A collaboration with the University of Dundee demonstrating that AI-assisted echocardiography analysis can systematically identify heart failure patients who were missed through conventional clinical pathways, enabling earlier diagnosis and treatment initiation.
Why Automated Echo Matters for Heart Failure
International guidelines place echocardiography at the center of HF diagnosis. Automation addresses the key barriers to guideline-concordant care.
Reducing LVEF Variability
International guidelines acknowledge that LVEF measurement is "subject to substantial variability." Automated analysis delivers consistent, reproducible EF, critical when treatment decisions hinge on whether EF is 39% or 41%.
Scaling with Demand
The guidelines call for more echo screening in people without symptoms. Automated measurement lets a lab take on more studies without adding the same number of specialist reading hours.
HFpEF: The Diagnostic Challenge
HFpEF remains the most difficult HF phenotype to diagnose, requiring integration of multiple echo parameters. The guidelines note "ongoing diagnostic uncertainty." Automated multi-parameter reporting helps clinicians apply the criteria consistently.
Frequently Asked Questions
Common questions about heart failure and AI echocardiography.
How common is heart failure?
Heart failure affects 1-2% of adults in developed countries and over 10% of those aged 70+. Approximately 64 million people are affected globally. Hospital admissions for HF are projected to increase 50% in the next 25 years due to aging populations.
What role does echocardiography play in heart failure diagnosis?
Echocardiography is the key investigation recommended by international guidelines for assessing cardiac function in heart failure. It determines the HF phenotype through LVEF measurement (HFrEF ≤40%, HFmrEF 41-49%, HFpEF ≥50%) and provides critical parameters including diastolic function, GLS, LA volume, and RV function.
How does AI echocardiography help with heart failure?
AI echocardiography measures every key parameter in the heart failure diagnostic pathway: LVEF, E/e', LA volume index, GLS, TAPSE, TR velocity, and LV mass index. This accurate, reproducible multi-parameter assessment distinguishes HFrEF, HFmrEF, and HFpEF, reduces measurement variability, and supports serial monitoring of treatment response.
Is there a CPT code for HFpEF analysis with Us2.ai?
The HFpEF analysis in Us2.v2 meets the CPT Category III 0932T code descriptor, so hospitals and physicians who use it can report 0932T. The provider decides what to code, and payment depends on the payer. Coding and Medicare payment detail is on the US reimbursement page.
What are the different types of heart failure?
International guidelines classify heart failure into three types by left ventricular ejection fraction (LVEF): HFrEF (reduced, LVEF ≤40%), HFmrEF (mildly reduced, LVEF 41-49%), and HFpEF (preserved, LVEF ≥50%). Each type has different diagnostic criteria and treatment pathways, all determined by echocardiography.
Can AI echocardiography identify the underlying cause of heart failure?
Classifying the phenotype is only the first step. Us2.ai quantifies LV wall thickness, LV mass, and the regional strain pattern from the same study, and its FDA-cleared cardiac amyloidosis module, Us2.ca, flags studies suggestive of cardiac amyloidosis, an under-recognized cause of HFpEF, in adults with increased wall thickness. Surfacing these markers alongside the phenotype helps clinicians distinguish hypertensive heart disease, hypertrophic cardiomyopathy, and cardiac amyloidosis, conditions that each follow a different care pathway.
What clinical evidence supports AI heart failure assessment?
Us2.ai's automated systolic and diastolic function analysis was validated in The Lancet Digital Health (Tromp et al., 2022) against expert human readers across four independent international cohorts, classifying systolic and diastolic dysfunction as accurately as expert sonographers, with less measurement variability.
Are Us2.ai heart failure measurements guideline-compliant and explainable?
Yes. Us2.ai reports its measurements against ESC and ASE/EACVI recommendations, including the LVEF thresholds that define HFrEF, HFmrEF, and HFpEF. Every measurement is traceable to the contour and beat it was derived from and can be reviewed and edited, which is essential for clinical trust, audit, and regulatory accountability under FDA clearance and EU MDR.
Echocardiography defines the HF phenotype. Accuracy makes the assessment reliable.
Complete Heart Failure Assessment, Every Phenotype
Us2.ai measures LVEF, GLS, diastolic function, and the full structural assessment with the accuracy and reproducibility that distinguishes HFrEF, HFmrEF, and HFpEF, automatically on every scan.