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.

Weikert, T., Francone, M., Abbara, S., Baessler, B., Choi, B. W., Gutberlet, M., Hecht, E. M., Loewe, C., Mousseaux, E., Natale, L., Nikolaou, K., Ordovas, K. G., Peebles, C., Prieto, C., Salgado, R., Velthuis, B., Vliegenthart, R., Bremerich, J., Leiner, T., Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges, <<EUROPEAN RADIOLOGY>>, 1; 31 (6): 3909-3922. [doi:10.1007/s00330-020-07417-0] [http://hdl.handle.net/10807/212948]

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

Natale, Luigi;
2021

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.
Inglese
Weikert, T., Francone, M., Abbara, S., Baessler, B., Choi, B. W., Gutberlet, M., Hecht, E. M., Loewe, C., Mousseaux, E., Natale, L., Nikolaou, K., Ordovas, K. G., Peebles, C., Prieto, C., Salgado, R., Velthuis, B., Vliegenthart, R., Bremerich, J., Leiner, T., Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges, <>, 1; 31 (6): 3909-3922. [doi:10.1007/s00330-020-07417-0] [http://hdl.handle.net/10807/212948]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/212948
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 5
social impact