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Automate the Heart Failure Echo Workup

Echocardiography is the central diagnostic tool in heart failure. Us2.ai delivers every parameter recommended in international guidelines (LVEF, GLS, diastolic function, and more) automatically on every scan.

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.

64M
people affected globally

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.

~20%
1-year mortality

One in five heart failure patients die within a year of diagnosis. Five-year mortality reaches 53–67%, worse than many cancers.

>45%
readmission at 1 year

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 Diagnostic Tool

International guidelines classify heart failure by LVEF. Echocardiography defines the phenotype and directly determines the treatment pathway.

≤40%
HFrEF
Reduced ejection fraction. Strongest evidence base for life-saving therapies (ARNI, beta-blockers, MRA, SGLT2i).
41–49%
HFmrEF
Mildly reduced. Emerging evidence supports HFrEF-type therapies. Requires accurate EF measurement to distinguish.
≥50%
HFpEF
Preserved. Diagnosis requires multiple echo parameters: E/e', LA volume, TR velocity, GLS, and LV mass index.

Every Parameter in the Heart Failure Diagnostic Pathway, Automatically

The Heart Failure diagnosis starts with symptoms and natriuretic peptides, then hinges on echocardiography. Us2.ai automates the entire echo assessment.

1

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.

Primary Classification
2

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.

HFpEF Diagnosis
3

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.

Early Detection
4

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.

Complete Workup
Serial Monitoring Built In

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.

Us2.ai left ventricle longitudinal measurements showing LVEF, LVEDV, and LVESV values across serial studies with trend graph

AI-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.

ESC Heart Failure 2024

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.

AI-powered HF detection: Identifies missed heart failure patients from electronic health records
Richer echo data: AI enriches dataset with critical parameters to support HF diagnosis in line with clinical guidelines
HF subtype classification: Distinguishes HFrEF from HFpEF at scale
Validated accuracy: 100% sensitivity, 94% specificity vs. manual diagnosis
Built for real-world use: Faster trial recruitment, earlier diagnosis, better surveillance
View Heart Failure Publications

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

HF admissions are projected to rise 50% in 25 years. The guidelines call for more screening in asymptomatic subjects. Automated echo analysis is the only way to meet growing demand without proportionally growing the specialist workforce.

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.

Echocardiography defines the HF phenotype. Automation makes it scalable.

Guideline-Complete Echo Reports, Zero Extra Clicks

Us2.ai delivers LVEF, GLS, diastolic function, and the full structural assessment, automatically on every scan. The complete heart failure workup, built in.

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