Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, curren
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Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges Thomas Weikert 1 & Marco Francone 2 & Suhny Abbara 3 & Bettina Baessler 4 & Byoung Wook Choi 5 & Matthias Gutberlet 6 & Elizabeth M. Hecht 7 & Christian Loewe 8 & Elie Mousseaux 9 & Luigi Natale 10 & Konstantin Nikolaou 11 & Karen G. Ordovas 12 & Charles Peebles 13 & Claudia Prieto 14 & Rodrigo Salgado 15 & Birgitta Velthuis 16 & Rozemarijn Vliegenthart 17 & Jens Bremerich 1 & Tim Leiner 16 Received: 25 June 2020 / Revised: 1 September 2020 / Accepted: 13 October 2020 # The Author(s) 2020
Abstract Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general. Key Points • Development and clinical implementation of machine learning in cardiovascular imaging is a multidisciplinary pursuit. • Based on existing study quality standard frameworks such as SPIRIT and STARD, we propose a list of quality criteria for ML studies in radiology. • The cardiovascular imaging research community should strive for the compilation of multicenter datasets for the development, evaluation, and benchmarking of ML algorithms. Keywords Artificial intelligence . Machine learning . Diagnostic techniques, cardiovascular . Radiology . Consensus Abbreviations AI Artificial intelligence ASCI Asian Society of Cardiac Imaging Endorsed by: Asian Society of Cardiac Imaging (ASCI), European Society of Medical Imaging Informatics (EUSOMII), North American Society for Cardiovascular Imaging (NASCI), Society of Cardiovascular Computed Tomography (SCCT), and Society of Cardiovascular Magnetic Resonance (SCMR), and Society for Magnetic Resonance Angiography (SMRA). * Thomas Weikert [email protected] Extended author information available on the last page of the article
AUC CONSORT CPU CT DCNN DL DSC ECG ESCR EUSOMII FDA FFR FN
Area under the curve Consolidated Standards of Reporting Trials Central processing unit Computed tomography Deep convolutional neural network Deep learning Dice similarity coefficient Electrocardiography European Society of Cardiovascular Radiology European Society of
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