Research services

Early detection of heart conditions can make a significant difference in a person's quality of life and longevity.

The ECG Core Lab at Mayo Clinic is a global leader in AI-powered ECG diagnostics, innovation and cardiac care.

ECG analysis services

We offer a wide range of services and expertise for researchers, industry partners and healthcare professionals who need research ECG analysis, whether it's for 10 ECG signals or 10,000. Our scientific engineering team builds, models and validates customized AI algorithms to suit each partner's needs.

Services include:

  • AI ECG algorithms: Developing and validating innovative ECG algorithms for early detection and risk prediction of cardiovascular conditions.
  • Expert interpretation and analysis: Expert interpretation and analysis of standard ambulatory ECG recordings and wearable device ECG recordings.
  • Clinical trial support: Standardized ECG assessments, reporting and adjudication for multicenter clinical studies, ensuring consistency and precision in ECG data interpretation.
  • Collaborative partnerships: Building global partnerships with academic institutions, researchers and industry to validate and implement new ECG technologies for diverse populations.
  • Education and training: Empowering clinicians and researchers with resources and knowledge to effectively integrate AI into their practices.
  • Operational efficiency: Centralized ECG analysis to reduce redundancy, optimize resources and enhance regulatory compliance.
  • Risk stratification models: Personalized risk predictions for conditions such as post-weight-loss atrial fibrillation and biological cardiac age.
  • Real-time analysis: Seamless integration into clinical workflows, enabling real-time decision-making and improved care.

AI algorithms

The ECG Core Lab has built nine AI algorithms that are used to validate research ECG data for Mayo Clinic and external partners:

  • Probability of low (less than 35%) left ventricular ejection fraction (LVEF).
  • Probability of detection of hypertrophic cardiomyopathy (HCM).
  • Probability of atrial fibrillation (AF), including silent AF.
  • Probability of moderate to severe aortic stenosis.
  • ECG age.
  • Probability of the patient being male.
  • Probability of detection of cardiac amyloidosis.
  • Probability of detection of liver cirrhosis.
  • Left ventricular diastolic function grade 0 to IV.

We also provide scientific assistance, direction with analysis, and publication support of any algorithm results and interpretation.

Related publications

Artificial intelligence electrocardiogram as a novel screening tool to detect a newly abnormal left ventricular ejection fraction after anthracycline-based cancer therapy. European Journal of Preventive Cardiology. 2024.

Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction. NPJ Digital Medicine. 2023.

Artificial intelligence-enabled electrocardiogram in the detection of patients at risk of atrial secondary mitral regurgitation. Circulation: Arrhythmia and Electrophysiology. 2023.

Machine-learning-derived heart and brain age are independently associated with cognition. European Journal of Neurology. 2023.

Fully automated artificial intelligence assessment of aortic stenosis by echocardiography. Journal of the American Society of Echocardiography. 2023.

Physiological age by artificial intelligence-enhanced electrocardiograms as a novel risk factor of mortality in kidney transplant candidates. Transplantation. 2023.

Correlation between artificial intelligence-enabled electrocardiogram and echocardiographic features in aortic stenosis. European Heart Journal: Digital Health. 2023.

Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model. European Heart Journal: Digital Health. 2023.

Assessing and mitigating bias in medical artificial intelligence: The effects of race and ethnicity on a deep learning model for ECG analysis. Circulation: Arrhythmia and Electrophysiology. 2020.

Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. Journal of the American College of Cardiology. 2020.

Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circulation: Arrhythmia and Electrophysiology. 2019.

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet. 2019.

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nature Medicine. 2019.

Novel bloodless potassium determination using a signal-processed single-lead ECG. Journal of the American Heart Association. 2016.