Evidence map›Paper›PMID 33864509›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2021

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT.

Riemer H J A Slart, Michelle C Williams, Luis Eduardo Juarez-Orozco, Christoph Rischpler, Marc R Dweck, Andor W J M Glaudemans, Alessia Gimelli, Panagiotis Georgoulias, Olivier Gheysens, Oliver Gaemperli and 12 more

Abstract readConsensus Statement
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Guideline
  4. Article
  5. Review
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  7. Article
  8. Review
  9. 2025 ESC/EACTS Valvular Guidelines: imaging perspective over a new treatment direction.European heart journal. Imaging methods and practice · 2026
    Article
  10. Review
  11. Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025
    Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
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  18. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Riemer H J A SlartMedical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands. r.h.j.a.slart@umcg.nl.ORCID http://orcid.org/0000-0002-5565-1164
Michelle C WilliamsBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Luis Eduardo Juarez-OrozcoDepartment of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Christoph RischplerDepartment of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Marc R DweckBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Andor W J M GlaudemansMedical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
Alessia GimelliFondazione Toscana G. Monasterio, Pisa, Italy.
Panagiotis GeorgouliasDepartment of Nuclear Medicine, Faculty of Medicine, University of Thessaly, University Hospital of Larissa, Larissa, Greece.
Olivier GheysensDepartment of Nuclear Medicine, Cliniques Universitaires Saint-Luc and Institute of Clinical and Experimental Research (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Oliver GaemperliHeartClinic, Hirslanden Hospital Zurich, Zurich, Switzerland.
Gilbert HabibAPHM, Cardiology Department, La Timone Hospital, Marseille, France.
Roland HustinxDivision of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, ULiège, Liège, Belgium.
Bernard CosynsDepartment of Cardiology, Centrum voor Hart en Vaatziekten, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, 1090, Brussels, Belgium.
Hein J VerberneDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands.
Fabien HyafilDepartment of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F-75015, Paris, France.
Paola A ErbaMedical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
Mark LubberinkDepartment of Surgical Sciences/Radiology, Uppsala University, Uppsala, Sweden.
Piotr SlomkaDepartment of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Ivana IšgumDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands.
Dimitris VisvikisINSERM, LaTIM, UMR 1101, Univ Brest, Brest, France.
Márton KolossváryMTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary.
Antti SarasteTurku PET Centre, Turku University Hospital, University of Turku, Turku, Finland.

Funding

British Heart Foundation FS/13/77/30488British Heart Foundation FS/14/78/31020British Heart Foundation FS/ICRF/20/26002
6 · The paper itself

Abstract

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Indexed as

Nuclear MedicinePositron Emission Tomography Computed TomographyArtificial IntelligenceHumansPositron-Emission TomographyTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedCardiovascularDeep learningMachine learningMultimodality imagingPosition paper

Identifiers

PMID33864509
PMCPMC8113178

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.