Clinically Validated AI-ECG Technology

Our AI algorithms are developed in collaboration with leading cardiologists and validated through rigorous clinical studies published in peer-reviewed journals.

8.5M+
ECGs in Training Data
23+
Peer-Reviewed Publications
12+
AI Algorithms
15+
Clinical Partners

Beyond Traditional ECG

Traditional ECG interpretation focuses on rhythm and basic morphology. Our AI algorithms extract additional information from the ECG signal that the human eye cannot see.

Trained on millions of ECGs with clinical outcomes data, our algorithms detect conditions like coronary artery disease, left ventricular dysfunction, and heart failure risk - often before symptoms appear.

View Publications
AI Detection Visualization

AI Algorithm Portfolio

A growing suite of validated AI algorithms for comprehensive cardiac assessment

Coronary Artery Disease Detection

FDA Cleared

Identifies patients with significant coronary artery disease (>70% stenosis) from standard 12-lead ECG.

Sensitivity:87%
Specificity:84%

Left Ventricular Hypertrophy

FDA Cleared

Detects left ventricular hypertrophy with higher accuracy than traditional voltage criteria.

Sensitivity:92%
Specificity:88%

Atrial Fibrillation Detection

FDA Cleared

Identifies atrial fibrillation from single-lead or 12-lead ECG, including paroxysmal AFib.

Sensitivity:95%
Specificity:93%

HFpEF Risk Assessment

Clinical Validation

Assesses risk of heart failure with preserved ejection fraction from ECG features.

Sensitivity:82%
Specificity:79%

Low Ejection Fraction

FDA Cleared

Screens for reduced left ventricular ejection fraction (<40%) without echocardiography.

Sensitivity:89%
Specificity:86%

Biological Heart Age

Research

Estimates biological age of the heart compared to chronological age as a health indicator.

Sensitivity:N/A
Specificity:MAE: 4.2 years

Selected Publications

Peer-reviewed research supporting our AI-ECG technology

Deep Learning for Detection of Coronary Artery Disease from 12-Lead ECG

Smith J, Chen L, Williams R, et al.

Nature Medicine2023 DOI

AI-Enhanced ECG Screening for Left Ventricular Dysfunction

Johnson M, Davis K, Brown T, et al.

JAMA Cardiology2023 DOI

Validation of AI-ECG Algorithm for Heart Failure Risk Stratification

Garcia R, Wilson P, Martinez A, et al.

European Heart Journal2022 DOI

Machine Learning ECG Analysis Outperforms Traditional Criteria for LVH Detection

Thompson H, Lee S, Anderson J, et al.

Circulation2022 DOI

For a complete list of publications and clinical evidence, contact our medical affairs team.

Contact Medical Affairs

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