S
Steffen E. Petersen
Researcher at Queen Mary University of London
Publications - 513
Citations - 26446
Steffen E. Petersen is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 58, co-authored 415 publications receiving 16004 citations. Previous affiliations of Steffen E. Petersen include Aarhus University Hospital & University of Mainz.
Papers
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Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods
Musa Abdulkareem,Asma Kenawy,Elisa Rauseo,Aaron M. Lee,Alireza Sojoudi,Alborz Amir-Khalili,Karim Lekadir,Alistair A. Young,Michael R. Barnes,P Barckow,Mohammed Y Khanji,Nay Aung,Steffen E. Petersen +12 more
TL;DR: Two supervised learning methods are applied, namely, the support vector machines (SVM) and the decision tree (DT) methods, are explored to develop predictive models for classifying pre-contrast cine SAX images as being a case of MI or healthy.
Journal ArticleDOI
Impact of cancer diagnosis on distribution and trends of cardiovascular hospitalizations in the USA between 2004 to 2017.
Ofer Kobo,Zahra Raisi-Estabragh,Sofie Gevaert,Jamal S. Rana,Harriette G.C. Van Spall,Ariel Roguin,Steffen E. Petersen,Bonnie Ky,Mamas A. Mamas +8 more
TL;DR: The distribution, trends of admissions, and in-hospital mortality associated with key cardiovascular diseases among cancer patients in the USA between 2004 to 2017 is described and primary cardiovascular admissions in patients with cancer is increasing.
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Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging
L Szabo,Zahra Raisi-Estabragh,Ahmed Salih,Celeste McCracken,E. Ruiz Pujadas,Polyxeni Gkontra,Mate Kiss,Pál Maurovich-Horváth,Hajnalka Vágó,Béla Merkely,Aaron M. Lee,Karim Lekadir,Steffen E. Petersen +12 more
TL;DR: In this paper , the authors provide a summary of the concepts involved in developing a "trustworthy" AI system, and describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging.
Posted Content
3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
Rahman Attar,Marco Pereanez,Christopher Bowles,Stefan K. Piechnik,Stefan Neubauer,Steffen E. Petersen,Alejandro F. Frangi +6 more
TL;DR: This work proposes a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters and validated the proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles.
Journal ArticleDOI
Stress myocardial perfusion cardiac magnetic resonance imaging vs. coronary CT angiography in the diagnostic work-up of patients with stable chest pain: comparative effectiveness and costs
Tessa S. S. Genders,Steffen E. Petersen,Francesca Pugliese,Amardeep Ghosh Dastidar,Kirsten E. Fleischmann,Koen Nieman,M. G. Myriam Hunink +6 more
TL;DR: Author(s): Genders, Tessa S; Petersen, Steffen E; Pugliese, Francesca; Dastidar, Amardeep; Fleischmann, Kirsten E; Nieman, Koen; Hunink, Myriam.