Evidence map›Paper›PMID 31023305›Full record

ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2019

Fully automated quantification of biventricular volumes and function in cardiovascular magnetic resonance: applicability to clinical routine settings.

Sören J Backhaus, Wieland Staab, Michael Steinmetz, Christian O Ritter, Joachim Lotz, Gerd Hasenfuß, Andreas Schuster, Johannes T Kowallick

Open access · goldAbstract read
In one paragraph

Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
4.6field-weighted citation impact, top 5% 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

30 citing papers in PubMed, 45 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022
    Article
  18. Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022
    Article
  19. Article
  20. Article
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

8 authors at 4 institutions in 2 countries.

Sören J BackhausDepartment of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany.
Wieland StaabGerman Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany.
Michael SteinmetzGerman Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany.
Christian O RitterGerman Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany.
Joachim LotzGerman Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany.
Gerd HasenfußDepartment of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany.
Andreas SchusterDepartment of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany.
Johannes T KowallickGerman Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany. johannes.kowallick@med.uni-goettingen.de.
German Centre for Cardiovascular Research · DEUniversity of Göttingen · DERoyal North Shore Hospital · AUUniversitätsmedizin Göttingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiovascular magnetic resonance (CMR) represents the clinical gold standard for the assessment of biventricular morphology and function. Since manual post-processing is time-consuming and prone to observer variability, efforts have been directed towards automated volumetric quantification. In this study, we sought to validate the accuracy of a novel approach providing fully automated quantification of biventricular volumes and function in a "real-world" clinical setting.

methodsThree-hundred CMR examinations were randomly selected from the local data base. Fully automated quantification of left ventricular (LV) mass, LV and right ventricular (RV) end-diastolic and end-systolic volumes (EDV/ESV), stroke volume (SV) and ejection fraction (EF) were performed overnight using commercially available software (suiteHEART®, Neosoft, Pewaukee, Wisconsin, USA). Parameters were compared to manual assessments (QMass®, Medis Medical Imaging Systems, Leiden, Netherlands). Sub-group analyses were further performed according to image quality, scanner field strength, the presence of implanted aortic valves and repaired Tetralogy of Fallot (ToF).

resultsBiventricular automated segmentation was feasible in all 300 cases. Overall agreement between fully automated and manually derived LV parameters was good (LV-EF: intra-class correlation coefficient [ICC] 0.95; bias - 2.5% [SD 5.9%]), whilst RV agreement was lower (RV-EF: ICC 0.72; bias 5.8% [SD 9.6%]). Lowest agreement was observed in case of severely altered anatomy, e.g. marked RV dilation but normal LV dimensions in repaired ToF (LV parameters ICC 0.73-0.91; RV parameters ICC 0.41-0.94) and/or reduced image quality (LV parameters ICC 0.86-0.95; RV parameters ICC 0.56-0.91), which was more common on 3.0 T than on 1.5 T.

conclusionsFully automated assessments of biventricular morphology and function is robust and accurate in a clinical routine setting with good image quality and can be performed without any user interaction. However, in case of demanding anatomy (e.g. repaired ToF, severe LV hypertrophy) or reduced image quality, quality check and manual re-contouring are still required.

Indexed as

Stroke VolumeVentricular Function, LeftVentricular Function, RightAdultAgedAutomationDatabases, FactualFeasibility StudiesFemaleHeart DiseasesHeart VentriclesHumansImage Interpretation, Computer-AssistedMagnetic Resonance Imaging, CineMaleMiddle Aged

Identifiers

PMID31023305
PMCPMC8059518
OpenAlexW2944902362

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.