Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges

Thomas Weikert, Marco Francone, Suhny Abbara, Bettina Baessler, Byoung Wook Choi, Matthias Gutberlet, Elizabeth M. Hecht, Christian Loewe, Elie Mousseaux, Luigi Natale, Konstantin Nikolaou, Karen G. Ordovas, Charles Peebles, Claudia Prieto, Rodrigo Salgado, Birgitta Velthuis, Rozemarijn Vliegenthart, Jens Bremerich, Tim Leiner

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)3909-3922
Number of pages14
JournalEuropean Radiology
Volume31
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

  • Artificial intelligence
  • Consensus
  • Diagnostic techniques, cardiovascular
  • Machine learning
  • Radiology

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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