Advancing the understanding of heart disease detection through rigorous research and peer-reviewed publications.
Most patients with heart disease are asymptomatic until advanced stages.
Physicians have limited front-line technology options to detect heart disease.
Most advanced diagnostic tests are expensive and require specialist referral.
Payors often discourage advanced diagnostics for asymptomatic patients.
License agreements for AI cardiovascular algorithms developed using millions of ECG records.
Multi-year collaboration to develop AI-based ECG algorithms for cardiac detection.
Clinical studies validating ECG-based detection of left ventricular dysfunction.
Our AI-ECG algorithms are backed by rigorous peer-reviewed research published in the world's leading cardiology journals.
A quantitative prediction of myocardial relaxation can be performed using easily obtained clinical and ECG features. This cost-effective strategy may be a valuable first clinical step for assessing LV dysfunction.
Read PublicationHeartBEiT, a vision-based transformer model pre-trained on 8.5 million ECGs, significantly outperforms standard CNN architectures for cardiac diagnosis.
Read PublicationAI-driven study redefines right heart health assessment with novel predictive model using standard 12-lead ECG.
Read PublicationDeep-learning on the 12-lead ECG alone can accurately predict new-onset cardiomyopathy in patients with PVCs independent of PVC burden.
Read PublicationMulti-center retrospective cohort study applying deep learning to electrocardiograms for early detection of valvular heart disease.
Read PublicationWavelet-based ECG signal processing enables detection of diastolic dysfunction that was previously only detectable via echocardiography.
Read PublicationRequest a demo to see how HeartSciences AI-ECG technology can enhance early detection of heart disease in your practice or health system.
Walk through our 23 peer-reviewed publications and clinical validation data.
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