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Samuel F Friedman

Showing results (1-10 of 20) with videos related to

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JACC. Clinical Electrophysiology|March 6, 2026
Electrocardiogram-Based Artificial Intelligence to Predict Recurrence After Cardioversion for Newly Diagnosed Atrial FibrillationKeisuke Usuda, Samuel F Friedman, Thomas Sommers, et al.
Circulation. Arrhythmia and Electrophysiology|August 28, 2024
Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After StrokeShaan Khurshid, Samuel F Friedman, Shinwan Kany, et al.
Medrxiv : the Preprint Server for Health Sciences|August 26, 2024
Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial FibrillationShinwan Kany, Joel T Rämö, Samuel F Friedman, et al.
Circulation|September 2, 2024
Rare Genetic Variants in <i>LDLR</i>, <i>APOB</i>, and <i>PCSK9</i> Are Associated With Aortic StenosisJoel T Rämö, Sean J Jurgens, Shinwan Kany, et al.
JAMA Cardiology|March 13, 2024
Cardiovascular Significance and Genetics of Epicardial and Pericardial AdiposityJoel T Rämö, Shinwan Kany, Cody R Hou, et al.
Medrxiv : the Preprint Server for Health Sciences|July 28, 2023
The Cardiovascular Impact and Genetics of Pericardial AdiposityJoel T Rämö, Shinwan Kany, Cody R Hou, et al.
JACC. Advances|June 18, 2026
A Machine Learning Driven Approach to Quantifying Coronary Artery TortuosityJose Roberto Tello Ayala, Kelvin Supriami, Siddharth Swaroop, et al.
NPJ Digital Medicine|February 22, 2025
A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogramMostafa A Al-Alusi, Samuel F Friedman, Shinwan Kany, et al.
JACC. Advances|August 1, 2025
Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery DiseaseShinwan Kany, Samuel F Friedman, Mostafa Al-Alusi, et al.
Journal of the American College of Cardiology|May 13, 2026
ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 PatientsRahul Mahajan, Danielle F Pace, Samuel F Friedman, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

Sort By:
Pageof 2
JACC. Clinical Electrophysiology|March 6, 2026
Electrocardiogram-Based Artificial Intelligence to Predict Recurrence After Cardioversion for Newly Diagnosed Atrial FibrillationKeisuke Usuda, Samuel F Friedman, Thomas Sommers, et al.
Circulation. Arrhythmia and Electrophysiology|August 28, 2024
Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After StrokeShaan Khurshid, Samuel F Friedman, Shinwan Kany, et al.
Medrxiv : the Preprint Server for Health Sciences|August 26, 2024
Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial FibrillationShinwan Kany, Joel T Rämö, Samuel F Friedman, et al.
Circulation|September 2, 2024
Rare Genetic Variants in <i>LDLR</i>, <i>APOB</i>, and <i>PCSK9</i> Are Associated With Aortic StenosisJoel T Rämö, Sean J Jurgens, Shinwan Kany, et al.
JAMA Cardiology|March 13, 2024
Cardiovascular Significance and Genetics of Epicardial and Pericardial AdiposityJoel T Rämö, Shinwan Kany, Cody R Hou, et al.
Medrxiv : the Preprint Server for Health Sciences|July 28, 2023
The Cardiovascular Impact and Genetics of Pericardial AdiposityJoel T Rämö, Shinwan Kany, Cody R Hou, et al.
JACC. Advances|June 18, 2026
A Machine Learning Driven Approach to Quantifying Coronary Artery TortuosityJose Roberto Tello Ayala, Kelvin Supriami, Siddharth Swaroop, et al.
NPJ Digital Medicine|February 22, 2025
A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogramMostafa A Al-Alusi, Samuel F Friedman, Shinwan Kany, et al.
JACC. Advances|August 1, 2025
Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery DiseaseShinwan Kany, Samuel F Friedman, Mostafa Al-Alusi, et al.
Journal of the American College of Cardiology|May 13, 2026
ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 PatientsRahul Mahajan, Danielle F Pace, Samuel F Friedman, et al.
Pageof 2