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Presentation TCT 2025

TCT 965: Can Machine Learning Aid the Evaluation of the Risks of Bleeding vs Thrombotic Events in Patients at High Bleeding Risk After PCI?

Presenter: Hao-Yu Wang

October 28, 2025

Presentation TCT 2025

TCT 1015: External Validation of the xDAPT Machine Learning Model for Ischemic and Bleeding Risk After Percutaneous Coronary Intervention

Presenter: Davide Cao

October 28, 2025

Presentation TCT 2025

TCT 916: Machine Learning to Predict Neurological Outcome after Out-of-hospital Cardiac Arrest

Presenter: Johannes Michael Altstidl

October 27, 2025

Presentation TCT 2025

TCT 476: Synergistic Impact of Insulin Resistance and Hepatic Fibrosis on Cardiovascular Events in Patients with Coronary Artery Disease: A Machine Learning Subtyping and Interaction Analysis

Presenter: Zhihao Zheng

October 27, 2025

Presentation TCT 2025

TCT 433: Can Major Cardiovascular Adverse Events (MACE) be Predicted? A Systematic Review and Meta-Analysis Exploring the Use of Machine Learning (ML) with Coronary Computed Tomography Angiography (CCTA)

Presenter: Saif Syed

October 27, 2025

Presentation TCT 2025

Featured Innovation: Automated Diagnosis and Classification of Aortic Stenosis from A Single Echocardiographic View: A Novel Machine Learning Approach

Presenter: Mayooran Namasivayam

October 26, 2025

Presentation TCT 2025

TCT 149: Prediction of Percutaneous Coronary Intervention Success in Moderate-to-severe Coronary Artery Calcification Patients Using Machine Learning Based on Coronary Angiography

Presenter: Zixiang Ye

October 26, 2025

Presentation TCT 2025

TCT 1059: Prediction of Patient Radiation Dose During Chronic Total Occlusion Percutaneous Coronary Intervention: A Machine Learning Augmented Analysis

Presenter: Pedro Carvalho

October 26, 2025

Presentation NY Valves 2025

Prediction of Early and Long-term Mortality After Transcatheter Aortic Valve Implantation in Australia: A Machine-learning Based Risk Prediction Model (PREDICT-TAVI)

Presenter: Hari Prakash Sritharan

June 27, 2025

Presentation THT 2025

Prospective Validation of Machine Learning in Risk Stratification of Patients Treated for Cardiogenic Shock

Presenter: Shuojohn Li

February 11, 2025

Presentation TCT 2024

Prediction of In-Hospital Mortality Following TAVI Using the FRANCE-TAVI Registry: A Machine Learning Approach

Presenter: Walid Ben-Ali

October 30, 2024

Presentation TCT 2024

TCT 264: Improving Risk Stratification of SCAI Stages of Cardiogenic Shock With Machine Learning-Based Phenotyping

Presenter: Elric Zweck

October 29, 2024

Presentation TCT 2024

TCT 192: Beta-Blocker Efficacy in Acute Myocardial Infarction: A Machine Learning Approach Using the KAMIR-NIH Dataset

Presenter: Woo Jin Ahn

October 27, 2024

Presentation TCT 2024

TCT 210: Machine Learning Cluster Analysis Identifies Increased 12-Month Mortality Risk in Transcatheter Aortic Valve Replacement Recipients

Presenter: Thomas Meredith

October 27, 2024

Presentation NY Valves 2024

Aortic Shape Variants Defined by Unbiased Machine Learning Technique Predict Complications in Transcatheter Aortic Valve Replacement

Presenter: Michal Schafer

June 05, 2024

Presentation NY Valves 2024

Overview of the Role of Artificial Intelligence and Machine Learning for VHD Detection

Presenter: Timothy Joseph Poterucha

June 05, 2024

Presentation THT 2024

Multicenter Development and Validation of a Supervised Machine Learning Risk Model to Predict Right Ventricular Failure Following Mechanical Circulatory Support: The STOP-RVF Score

Presenter: Stavros G. Drakos

March 06, 2024

Presentation CTO 2024

Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning

Presenter: Athanasios Rempakos

February 03, 2024

Presentation TCT 2023

TCT 775: A Machine Learning Based Model Predicts Death in Patients with Severe Asymptomatic Aortic Stenosis

Presenter: APURVA BHARUCHA

October 26, 2023

Presentation TCT 2023

TCT 753: Development of a Machine Learning Based Futility Risk Model to Predict One Year Mortality in Transcatheter Aortic Valve Replacement

Presenter: APURVA BHARUCHA

October 26, 2023

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TCT 965: Can Machine Learning Aid the Evaluation of the Risks of Bleeding vs Thrombotic Events in Patients at High Bleeding Risk After PCI?

TCT 1015: External Validation of the xDAPT Machine Learning Model for Ischemic and Bleeding Risk After Percutaneous Coronary Intervention

TCT 916: Machine Learning to Predict Neurological Outcome after Out-of-hospital Cardiac Arrest

TCT 476: Synergistic Impact of Insulin Resistance and Hepatic Fibrosis on Cardiovascular Events in Patients with Coronary Artery Disease: A Machine Learning Subtyping and Interaction Analysis

TCT 433: Can Major Cardiovascular Adverse Events (MACE) be Predicted? A Systematic Review and Meta-Analysis Exploring the Use of Machine Learning (ML) with Coronary Computed Tomography Angiography (CCTA)

Featured Innovation: Automated Diagnosis and Classification of Aortic Stenosis from A Single Echocardiographic View: A Novel Machine Learning Approach

TCT 149: Prediction of Percutaneous Coronary Intervention Success in Moderate-to-severe Coronary Artery Calcification Patients Using Machine Learning Based on Coronary Angiography

TCT 1059: Prediction of Patient Radiation Dose During Chronic Total Occlusion Percutaneous Coronary Intervention: A Machine Learning Augmented Analysis

Prediction of Early and Long-term Mortality After Transcatheter Aortic Valve Implantation in Australia: A Machine-learning Based Risk Prediction Model (PREDICT-TAVI)

Prospective Validation of Machine Learning in Risk Stratification of Patients Treated for Cardiogenic Shock

Prediction of In-Hospital Mortality Following TAVI Using the FRANCE-TAVI Registry: A Machine Learning Approach

TCT 264: Improving Risk Stratification of SCAI Stages of Cardiogenic Shock With Machine Learning-Based Phenotyping

TCT 192: Beta-Blocker Efficacy in Acute Myocardial Infarction: A Machine Learning Approach Using the KAMIR-NIH Dataset

TCT 210: Machine Learning Cluster Analysis Identifies Increased 12-Month Mortality Risk in Transcatheter Aortic Valve Replacement Recipients

Aortic Shape Variants Defined by Unbiased Machine Learning Technique Predict Complications in Transcatheter Aortic Valve Replacement

Overview of the Role of Artificial Intelligence and Machine Learning for VHD Detection

Multicenter Development and Validation of a Supervised Machine Learning Risk Model to Predict Right Ventricular Failure Following Mechanical Circulatory Support: The STOP-RVF Score

Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning

TCT 775: A Machine Learning Based Model Predicts Death in Patients with Severe Asymptomatic Aortic Stenosis

TCT 753: Development of a Machine Learning Based Futility Risk Model to Predict One Year Mortality in Transcatheter Aortic Valve Replacement

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