Doctor Konrad Pieszko

Doctor Konrad Pieszko

University of Zielona Gora, Zielona Gora (Poland)

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Konrad Pieszko is a cardiologist in training, with background in computer programming and statistics. He studied medicine at Wroclaw Medical University, Poland and robotics and Technical University of Wroclaw, Poland. After completing his PhD degree at Poznań Medical University, Poland he started a research fellowship at Cedars Sinai Medical Center, Los Angeles, USA. His research interests are focused around the applications of artificial intelligence in cardiology. Privately, he is a happy husband and father of two children, he loves to spend his time travelling and enjoying the beauty of the nature.

Presentations Articles

Artificial intelligence to measure left atrial ejection fraction in transthoracic echocardiography videos and its usefulness to assess thromboembolic risk.

Event: EuroEcho-Imaging 2024

Topic: Morphology, Dimensions, Volumes and Mass

Session: More insights on the importance of lon left atrial function

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Left atrial appendage volume estimated by artificial intelligence predicts atrial fibrillation recurrence after cryobaloon ablation

Event: ESC Congress 2024

Topic: Morphology, Dimensions, Volumes and Mass

Session: Cardiac computed tomography: artificial intelligence, radiomics, and more

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Predicting the presence of left atrial appendage thrombus with clinical features and transthoracic measurements using machine learning

Event: ESC Congress 2022

Topic: Rhythm Control, Cardioversion

Session: Young Investigator Awards Session in Thrombosis

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Inflammatory and hematological markers as early outcome predictors in acute coronary syndrome patients:machine learning modeling approach vs. classical statistics.

Event: ESC Congress 2018

Topic: Cardiovascular Risk Assessment, Other

Session: Secondary prevention

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Doctor Konrad Pieszko

Biography

Contributor content

Artificial intelligence to measure left atrial ejection fraction in transthoracic echocardiography videos and its usefulness to assess thromboembolic risk.

Left atrial appendage volume estimated by artificial intelligence predicts atrial fibrillation recurrence after cryobaloon ablation

Predicting the presence of left atrial appendage thrombus with clinical features and transthoracic measurements using machine learning

Inflammatory and hematological markers as early outcome predictors in acute coronary syndrome patients:machine learning modeling approach vs. classical statistics.

ESC 365 is supported by

Doctor Konrad Pieszko Follow

Biography

Contributor content

Artificial intelligence to measure left atrial ejection fraction in transthoracic echocardiography videos and its usefulness to assess thromboembolic risk.

Left atrial appendage volume estimated by artificial intelligence predicts atrial fibrillation recurrence after cryobaloon ablation

Predicting the presence of left atrial appendage thrombus with clinical features and transthoracic measurements using machine learning

Inflammatory and hematological markers as early outcome predictors in acute coronary syndrome patients:machine learning modeling approach vs. classical statistics.

ESC 365 is supported by

Doctor Konrad Pieszko