Jeffrey Ruhl

NewYork–Presbyterian Hospital — United States

Medicine · Health Sciences

5h-index124citations27works8.12yr avg
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Research Topics

Cardiac Valve Diseases and Treatments(7), Atrial Fibrillation Management and Outcomes(6), Cardiac Imaging and Diagnostics(6), ECG Monitoring and Analysis(5), Lipoproteins and Cardiovascular Health(3)

Publications27 total

TCT-71 Detection of Light Chain Amyloidosis Cardiomyopathy (AL-CM) by Artificial Intelligence Analysis of ECG and Echocardiography
Journal of the American College of Cardiology·2025
Machine learning–enabled assessment of risk of drug-induced QT prolongation at the time of prescribing
Heart Rhythm·2025· 1 citedOpen Access
Detecting structural heart disease from electrocardiograms using AI
Nature·2025· 34 citedOpen Access
Predicting Cardiopulmonary Exercise Testing Performance in Patients Undergoing Transthoracic Echocardiography - An AI Based, Multimodal Model
2025Open Access
DEEP LEARNING FOR ECHO ANALYSIS, TRACKING, AND EVALUATION OF MITRAL REGURGITATION PROGRESSION (DELINEATE-MR-PROGRESSION)
Journal of the American College of Cardiology·2025Open Access
Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-regurgitation study
European Heart Journal·2025· 14 citedOpen Access
An Artificial Intelligence Model to Detect Abnormal Ejection Fraction from Non-Contrast Chest Computed Tomography: The CT-LVEF study
2025Open Access
Abstract 4143939: A large-scale multi-view deep learning-based assessment of left ventricular ejection fraction in echocardiography
Circulation·2024
Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR)
Circulation·2024· 40 citedOpen Access
ECHOSTAR DEEP LEARNING SYSTEM: A COMPREHENSIVE ANALYSIS OF AORTIC VALVE FROM ECHO DOPPLER IMAGES
Journal of the American College of Cardiology·2024Open Access
DELINEATE-MR: DEEP LEARNING FOR AUTOMATED ASSESSMENT OF MITRAL REGURGITATION FROM ECHOCARDIOGRAPHY
Journal of the American College of Cardiology·2024
Abstract 14647: EchoNext: An ECG-Based Deep Learning Model to Detect Structural Heart Disease
Circulation·2023· 2 cited
Abstract 11000: Deep Learning Prediction of New-Onset Atrial Fibrillation Using Echocardiography Videos
Circulation·2022· 1 cited
Abstract 11452: EHR-Based Machine Learning Model Predicts Drug-Induced QT Prolongation With Superior Performance Compared to Clinical Risk Predictors
Circulation·2022
Abstract 11005: The Impact of Time Censoring on Machine Learning Models Which Identify Patients With Undiagnosed Cardiac Amyloidosis
Circulation·2022
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk
Journal of Electrocardiology·2022· 9 citedOpen Access
Session 2: AI in automated ECG analysisAI-based predictive and diagnostic electrocardiography
Journal of Electrocardiology·2022
Abstract 9599: Rechommend: An Ecg-Based Machine-Learning Approach for Identifying Patients at High-Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography
Circulation·2021
Abstract 9756: An ECG-Based Machine Learning Model for Predicting New Onset Atrial Fibrillation is Superior to Age and Clinical Variables in Selecting a Population at High Stroke Risk
Circulation·2021
Abstract 9536: Prediction of Drug-Induced QTc Prolongation With an ECG Based Machine Learning Model
Circulation·2021
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Frequent Co-authors

Christopher M. Haggerty(19), Dustin N. Hartzel(13), Brandon K. Fornwalt(11), Chris R. Kelsey(10), David P. vanMaanen(10), Ashley Beecy(10), Pierre Elias(10), Timothy J. Poterucha(10), Linyuan Jing(10), Sushravya Raghunath(10), John M. Pfeifer(9), Aaron S. Long(8), Joshua Finer(8), Noah Zimmerman(8), Linyuan Jing(7), Ruijun Chen(7), Daniel Rocha(6), Gil Metser(6), Eamon Duffy(6), Thomas Mawson(6)