TY - JOUR
T1 - Machine learning in cardiovascular radiology
T2 - ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges
AU - Weikert, Thomas
AU - Francone, Marco
AU - Abbara, Suhny
AU - Baessler, Bettina
AU - Choi, Byoung Wook
AU - Gutberlet, Matthias
AU - Hecht, Elizabeth M.
AU - Loewe, Christian
AU - Mousseaux, Elie
AU - Natale, Luigi
AU - Nikolaou, Konstantin
AU - Ordovas, Karen G.
AU - Peebles, Charles
AU - Prieto, Claudia
AU - Salgado, Rodrigo
AU - Velthuis, Birgitta
AU - Vliegenthart, Rozemarijn
AU - Bremerich, Jens
AU - Leiner, Tim
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Consensus
KW - Diagnostic techniques, cardiovascular
KW - Machine learning
KW - Radiology
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U2 - 10.1007/s00330-020-07417-0
DO - 10.1007/s00330-020-07417-0
M3 - Article
C2 - 33211147
AN - SCOPUS:85096316681
SN - 0938-7994
VL - 31
SP - 3909
EP - 3922
JO - European Radiology
JF - European Radiology
IS - 6
ER -