Advancing Heart Disease Detection
HeartSciences is committed to advancing the understanding of heart disease detection through rigorous research and peer-reviewed clinical publications.
Bridging the Diagnostic Gap
Traditional ECGs are limited in scope. AI-ECG technology unlocks hidden patterns in cardiac electrical signals, enabling earlier detection of heart disease.
Silent Disease
Most patients with heart disease are asymptomatic until advanced stages.
Limited Tools
Physicians have limited front-line technology options to detect heart disease.
Costly Diagnostics
Most advanced diagnostic tests are expensive and require specialist referral.
Payor Barriers
Payors often discourage advanced diagnostics for asymptomatic patients.
Key Statistics
Heart disease remains the leading cause of death globally. AI-ECG offers a path to earlier intervention.
Featured Publications
Peer-reviewed research from leading institutions
A foundational vision transformer improves diagnostic performance for electrocardiograms
HeartBEiT, a vision-based transformer model pre-trained on 8.5 million ECGs, demonstrates superior diagnostic performance at lower sample sizes.
Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features
JACC publication demonstrating machine learning can effectively assess diastolic function from ECG signals.
Quantitative Prediction of Right Ventricular Size and Function From the ECG
Novel AI algorithm for quantitative assessment of right ventricular parameters from standard ECG recordings.
See the Platform in Action
Request a personalized demo and discover how AI-ECG can transform your cardiac diagnostics workflow.