Literature DB >> 36006439

[Artificial intelligence and radiomics : Value in cardiac MRI].

Alexander Rau1, Martin Soschynski2, Jana Taron2, Philipp Ruile3, Christopher L Schlett2, Fabian Bamberg2, Tobias Krauss2.   

Abstract

CLINICAL/METHODICAL ISSUE: Cardiac diseases are the leading cause of death. Many diseases can be specifically treated once a valid diagnosis is established. Cardiac magnetic resonance imaging (MRI) plays a central role in the workup of many cardiac pathologies. However, image acquisition as well as interpretation and related secondary image evaluation are time-consuming and complex. STANDARD RADIOLOGICAL
METHODS: Cardiac MRI is becoming increasingly established in international guidelines for the evaluation of cardiac function and differential diagnosis of a wide variety of cardiac diseases. METHODOLOGICAL INNOVATIONS: Cardiac MRI has limited reproducibility due to the acquisition technique and interpretation of findings with complex secondary measurements. Artificial intelligence techniques and radiomics offer the potential to improve the acquisition, interpretation, and reproducibility of cardiac MRI. PERFORMANCE: Research suggests that artificial intelligence and radiomic analysis can improve cardiac MRI in terms of image acquisition and also diagnostic and prognostic value. Furthermore, the implementation of artificial intelligence and radiomics may result in the identification of new biomarkers. ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS: The implementation of artificial intelligence in cardiac MRI has great potential. However, the current level of evidence is still limited in some aspects; in particular there are too few prospective and large multicenter studies available. As a result, the algorithms developed are often not sufficiently validated scientifically and are not yet applied in clinical routine.
© 2022. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.

Entities:  

Keywords:  Cardiac magnetic resonance imaging; Data analysis; Deep learning; Image acquisition; Machine learning

Year:  2022        PMID: 36006439     DOI: 10.1007/s00117-022-01060-0

Source DB:  PubMed          Journal:  Radiologie (Heidelb)        ISSN: 2731-7048


  29 in total

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Review 2.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

3.  2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes.

Authors:  Juhani Knuuti; William Wijns; Antti Saraste; Davide Capodanno; Emanuele Barbato; Christian Funck-Brentano; Eva Prescott; Robert F Storey; Christi Deaton; Thomas Cuisset; Stefan Agewall; Kenneth Dickstein; Thor Edvardsen; Javier Escaned; Bernard J Gersh; Pavel Svitil; Martine Gilard; David Hasdai; Robert Hatala; Felix Mahfoud; Josep Masip; Claudio Muneretto; Marco Valgimigli; Stephan Achenbach; Jeroen J Bax
Journal:  Eur Heart J       Date:  2020-01-14       Impact factor: 29.983

4.  Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI.

Authors:  Nan Zhang; Guang Yang; Zhifan Gao; Chenchu Xu; Yanping Zhang; Rui Shi; Jennifer Keegan; Lei Xu; Heye Zhang; Zhanming Fan; David Firmin
Journal:  Radiology       Date:  2019-04-30       Impact factor: 11.105

5.  2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure.

Authors:  Theresa A McDonagh; Marco Metra; Marianna Adamo; Roy S Gardner; Andreas Baumbach; Michael Böhm; Haran Burri; Javed Butler; Jelena Čelutkienė; Ovidiu Chioncel; John G F Cleland; Andrew J S Coats; Maria G Crespo-Leiro; Dimitrios Farmakis; Martine Gilard; Stephane Heymans; Arno W Hoes; Tiny Jaarsma; Ewa A Jankowska; Mitja Lainscak; Carolyn S P Lam; Alexander R Lyon; John J V McMurray; Alexandre Mebazaa; Richard Mindham; Claudio Muneretto; Massimo Francesco Piepoli; Susanna Price; Giuseppe M C Rosano; Frank Ruschitzka; Anne Kathrine Skibelund
Journal:  Eur Heart J       Date:  2021-09-21       Impact factor: 29.983

6.  Whole-Body Magnetic Resonance Imaging in the Large Population-Based German National Cohort Study: Predictive Capability of Automated Image Quality Assessment for Protocol Repetitions.

Authors:  Christopher Schuppert; Ricarda von Krüchten; Jochen G Hirsch; Susanne Rospleszcz; Daniel C Hoinkiss; Sonja Selder; Alexander Köhn; Oyunbileg von Stackelberg; Annette Peters; Henry Völzke; Thomas Kröncke; Thoralf Niendorf; Michael Forsting; Norbert Hosten; Thomas Hendel; Tobias Pischon; Karl-Heinz Jöckel; Rudolf Kaaks; Fabian Bamberg; Hans-Ulrich Kauczor; Matthias Günther; Christopher L Schlett
Journal:  Invest Radiol       Date:  2022-02-21       Impact factor: 10.065

7.  Deep Learning-based Prescription of Cardiac MRI Planes.

Authors:  Kevin Blansit; Tara Retson; Evan Masutani; Naeim Bahrami; Albert Hsiao
Journal:  Radiol Artif Intell       Date:  2019-11-27

8.  Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort.

Authors:  Steffen E Petersen; Nay Aung; Mihir M Sanghvi; Filip Zemrak; Kenneth Fung; Jose Miguel Paiva; Jane M Francis; Mohammed Y Khanji; Elena Lukaschuk; Aaron M Lee; Valentina Carapella; Young Jin Kim; Paul Leeson; Stefan K Piechnik; Stefan Neubauer
Journal:  J Cardiovasc Magn Reson       Date:  2017-02-03       Impact factor: 5.364

9.  Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.

Authors:  Andreas Hauptmann; Simon Arridge; Felix Lucka; Vivek Muthurangu; Jennifer A Steeden
Journal:  Magn Reson Med       Date:  2018-09-08       Impact factor: 4.668

10.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Authors:  Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin
Journal:  Nat Commun       Date:  2014-06-03       Impact factor: 14.919

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