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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

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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

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