Evidence map›Paper›PMID 38671059›Full record

ArticleScientific reports2024

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [

Sacha Bors, Daniel Abler, Matthieu Dietz, Vincent Andrearczyk, Julien Fageot, Marie Nicod-Lalonde, Niklaus Schaefer, Robert DeKemp, Christel H Kamani, John O Prior and 1 more

Open access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
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

11 authors at 5 institutions in 3 countries.

Sacha BorsNuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland.
Daniel AblerDepartment of Oncology, Lausanne University Hospital, Lausanne, Switzerland.
Matthieu DietzINSERM U1060, CarMeN laboratory, University of Lyon, Lyon, France.
Vincent AndrearczykNuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland.
Julien FageotAudioVisual Communications Laboratory (LCAV), EPFL, Lausanne, Switzerland.
Marie Nicod-LalondeNuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland.
Niklaus SchaeferNuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland.
Robert DeKempDivision of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada.
Christel H KamaniDepartment of Cardiology, Lausanne University Hospital, Lausanne, Switzerland.
John O PriorNuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland. John.Prior@chuv.ch.
Adrien DepeursingeNuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland.
University of Lausanne · CHHES-SO Valais-Wallis · CHÉcole Polytechnique Fédérale de Lausanne · CHHospices Civils de Lyon · FRUniversity of Ottawa · CA

Funding

Hasler Stiftung 21042Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 205320_179069Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 205320_219430Swiss Cancer Research Foundation KFS-5549-02-2022-R
6 · The paper itself

Abstract

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [

Indexed as

Myocardial Perfusion ImagingPositron-Emission TomographyAgedArtificial IntelligenceCardiovascular DiseasesCoronary CirculationFemaleHumansMaleMiddle AgedNeural Networks, ComputerPrognosisRubidium RadioisotopesRubidium-82Rubidium Radioisotopes

Identifiers

PMID38671059
PMCPMC11053111
OpenAlexW4395676792

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.