Category Archives: Clinical

ECG-derived machine-learning models provide a cost-effective strategy for predicting echocardiographically defined patients subgroups at a high-risk of MACE and may aid in optimizing intervention strategies. Link to article: SURFACE ECG-BASED MACHINE LEARNING MODEL FOR PREDICTING PATIENT SUBGROUP AT A HIGH RISK FOR MAJOR ADVERSE CARDIAC EVENTS | Journal of the American College of Cardiology (jacc.org)

The study demonstrates MyoVista® wavECG™ ability to predict abnormal Calcium Score Heart disease is a global pandemic affecting at least 26 million people worldwide and continues to increase in prevalence due to increasing life expectancy in the general population. More effective front-line tools are needed that can assist in identifying patients earlier while also lowering […]

Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline-recommended CAC score categories […]

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 the presence of LV dysfunction and may potentially aid in the early diagnosis and management of heart failure patients. Link to article: Machine Learning Assessment of Left […]

Study demonstrates MyoVista® wavECG™ ability to cost-effectively detect LV Dysfunction SOUTHLAKE, Texas, August 18, 2020 – HeartSciences, a medical device company focused on advancing the field of electrocardiology through innovation, announced today the results of a multicenter prospective study conducted at 4 centers in North America enrolling a total of 1,202 subjects. The study was […]

The field of cardiovascular medicine has long been at the forefront of innovation. Innovations in data science, particularly machine learning (ML) and artificial intelligence (AI), have generated enthusiasm in the field to bring about transformative changes to cardiovascular care, replicating the disruption of accepted norms they brought about in other spheres of life (1). However, […]

Using ML algorithms, sensitivity of ewECG is suitable for application as a screening test for SBHF in apparent SAHF. Our data suggest ewECG could reduce the number of echocardiograms performed as part of a HF population screening program by 18-25%. Link to reference:  P3431Machine learning applied to energy waveform ECG for prediction of subclinical myocardial […]

M Piccirilli, S Shrestha, N Kagiyama, L Hu, H Kulkarni, P P Sengupta European Heart Journal, Volume 40, Issue Supplement_1, October 2019, ehz748.0769, https://doi.org/10.1093/eurheartj/ehz748.0769 Published: 21 October 2019 Abstract Background The impairment of myocardial relaxation is a strong predictor of all-cause mortality and has been proposed to be a potential tool for cardiovascular (CV) risk stratification. We investigated a novel signal-processed […]

Since the first report of the human electrocardiogram (ECG) by Augustus Waller in 1887 (1), its diagnostic use has continually expanded. Providing a window to the electric activity of the heart, the ECG allowed the documentation and classification of arrhythmias, soon followed by estimation of atrial and ventricular size (2). Some years later, dynamic changes […]

The spECG provides a robust prediction of abnormal myocardial relaxation. These data suggest a potential role for spECG as a novel screening strategy for identifying patients at risk for LVDD who would benefit undergoing echocardiographic evaluations. Link to article: Prediction of Abnormal Myocardial Relaxation From Signal Processed Surface ECG | Journal of the American College […]