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Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease

Journal of the American Heart Association, 2019-09, Vol.8 (17), p.e012788-e012788

2019

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Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features

Journal of the American College of Cardiology, 2020-08, Vol.76 (8), p.930-941

2020

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Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis

JACC. Cardiovascular imaging, 2019-02, Vol.12 (2), p.236-248

2019

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Echocardiographic assessment of mitral regurgitation

Journal of medical ultrasonics (2001), 2020-01, Vol.47 (1), p.59-70

2020

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A Network-Based “Phenomics” Approach for Discovering Patient Subtypes From High-Throughput Cardiac Imaging Data

JACC. Cardiovascular imaging, 2020-08, Vol.13 (8), p.1655-1670

2020

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A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound

EBioMedicine, 2020-04, Vol.54, p.102726-102726, Article 102726

2020

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Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review

Journal of the American College of Cardiology, 2019-03, Vol.73 (11), p.1317-1335

2019

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Machine Learning for Data-Driven Discovery

JACC. Cardiovascular imaging, 2019-04, Vol.12 (4), p.690-692

2019

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Machine learning for nuclear cardiology: The way forward

Journal of nuclear cardiology, 2019-10, Vol.26 (5), p.1755-1758

2019

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Artificial Intelligence in Cardiovascular Medicine

Current treatment options in cardiovascular medicine, 2019-05, Vol.21 (5), p.25-25, Article 25

2019

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Echocardiographic assessment of mitral regurgitation

Chōonpa igaku (2001), 2021, Vol.48 (4), p.151-163, Article JJMU.R.178

2021

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Solving coronary risk: time to feed machines some calcium (score) supplements

European heart journal, 2020-01, Vol.41 (3), p.368-370

2020

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Machine Learning for Data-Driven Discovery: The Rise and Relevance

JACC. Cardiovascular imaging, 2019-04, Vol.12 (4), p.690-692

2019

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PAPILLARY MUSCLE HYPERTROPHY MASQUERADING STEMI

Journal of the American College of Cardiology, 2020-03, Vol.75 (11), p.2547-2547

2020

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Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council

JACC. Cardiovascular imaging, 2020-09, Vol.13 (9), p.2017-2035

2020

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IDENTIFICATION OF VALVULAR HEART DISEASE FROM BODY SURFACE ELECTROCARDIOGRAM: A PROSPECTIVE MULTICENTER STUDY

Journal of the American College of Cardiology, 2022-03, Vol.79 (9), p.30

2022

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The Mechanics of Machine Learning: From a Concept to Value

Journal of the American Society of Echocardiography, 2018-12, Vol.31 (12), p.1285-1287

2018

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PHENOTYPIC PATTERNS OF LEFT VENTRICULAR DIASTOLIC FUNCTION IDENTIFIED USING CLUSTERING APPROACHES

Journal of the American College of Cardiology, 2019-03, Vol.73 (9), p.1515-1515

2019

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Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine

JACC. Cardiovascular imaging, 2020-05, Vol.13 (5), p.1119-1132

2020

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Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

Interventional cardiology (London), 2021-02, Vol.16

2021

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BALLOON AORTIC VALVULOPLASTY AS A BRIDGE TO AORTIC VALVE REPLACEMENT A CONTEMPORARY NATIONAL PERSPECTIVE

Journal of the American College of Cardiology, 2020-03, Vol.75 (11), p.1180-1180

2020

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A NETWORK-BASED APPROACH FOR DISCOVERING PATIENT PHENOTYPES FROM HIGH-THROUGHPUT CARDIAC IMAGING DATA

Journal of the American College of Cardiology, 2020-03, Vol.75 (11), p.1780-1780

2020

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Imaging Heart Failure With Artificial Intelligence: Improving the Realism of Synthetic Wisdom

Circulation. Cardiovascular imaging, 2018-04, Vol.11 (4), p.e007723-e007723

2018

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Evaluating a novel method of scoring multirater assessments of leader competencies

Consulting psychology journal, 2024-10

2024

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TOPOLOGICAL DATA ANALYSIS FOR QUANTIFYING INTER-PATIENT SIMILARITIES IN CARDIAC FUNCTION

Journal of the American College of Cardiology, 2019-03, Vol.73 (9), p.751-751

2019

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Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease

Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features

Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis

Echocardiographic assessment of mitral regurgitation

A Network-Based “Phenomics” Approach for Discovering Patient Subtypes From High-Throughput Cardiac Imaging Data

A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound

Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review

Machine Learning for Data-Driven Discovery

Machine learning for nuclear cardiology: The way forward

Artificial Intelligence in Cardiovascular Medicine

Echocardiographic assessment of mitral regurgitation

Solving coronary risk: time to feed machines some calcium (score) supplements

Machine Learning for Data-Driven Discovery: The Rise and Relevance

PAPILLARY MUSCLE HYPERTROPHY MASQUERADING STEMI

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council

IDENTIFICATION OF VALVULAR HEART DISEASE FROM BODY SURFACE ELECTROCARDIOGRAM: A PROSPECTIVE MULTICENTER STUDY

The Mechanics of Machine Learning: From a Concept to Value

PHENOTYPIC PATTERNS OF LEFT VENTRICULAR DIASTOLIC FUNCTION IDENTIFIED USING CLUSTERING APPROACHES

Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

BALLOON AORTIC VALVULOPLASTY AS A BRIDGE TO AORTIC VALVE REPLACEMENT A CONTEMPORARY NATIONAL PERSPECTIVE

A NETWORK-BASED APPROACH FOR DISCOVERING PATIENT PHENOTYPES FROM HIGH-THROUGHPUT CARDIAC IMAGING DATA

Imaging Heart Failure With Artificial Intelligence: Improving the Realism of Synthetic Wisdom

Evaluating a novel method of scoring multirater assessments of leader competencies

TOPOLOGICAL DATA ANALYSIS FOR QUANTIFYING INTER-PATIENT SIMILARITIES IN CARDIAC FUNCTION

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