Evidence map›Paper›PMID 33812855›Full record

ReviewJournal of cardiovascular computed tomography

Artificial intelligence in cardiovascular CT: Current status and future implications.

Andrew Lin, Márton Kolossváry, Manish Motwani, Ivana Išgum, Pál Maurovich-Horvat, Piotr J Slomka, Damini Dey

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of cardiovascular computed tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Segmentation and Volumetric Analysis of Heart from Cardiac CT Images.Cardiovascular engineering and technology · 2024
    Article
  12. Article
  13. Review
  14. Review
  15. Application and prospects of AI-based radiomics in ultrasound diagnosis.Visual computing for industry, biomedicine, and art · 2023
    Review
  16. Article
  17. Review
  18. Review
  19. Article
  20. 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

7 authors.

Andrew LinBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Márton KolossváryCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Manish MotwaniManchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom.
Ivana IšgumDepartment of Biomedical Engineering and Physics, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, Netherlands.
Pál Maurovich-HorvatMedical Imaging Centre, Semmelweis University, Budapest, Hungary.
Piotr J SlomkaArtificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Damini DeyBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: damini.dey@cshs.org.

Funding

Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT AngiographyR01HL148787 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2020 to 2023
$3.0M
Automated quantitative CT imaging of epicardial adipose tissue and risk of cardiac eventsR01HL133616 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2016 to 2019
$2.6M
Effect of Intensive Medical Treatment on Quantified Coronary Artery Plaque Components with Serial Coronary CTA in Women with Non-Obstructive CADR01HL151266 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI, TAMARAPPOO, BALAJI K. · 2020 to 2023
$1.6M
NHLBI NIH HHS R01 HL133616NHLBI NIH HHS R01 HL148787NHLBI NIH HHS R01 HL151266
6 · The paper itself

Abstract

Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction. By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways. This review covers state-of-the-art AI techniques in cardiovascular CT and the future role of AI as a clinical support tool.

Indexed as

Artificial IntelligenceMachine LearningAlgorithmsHumansPredictive Value of TestsTomography, X-Ray ComputedArtificial intelligenceCardiovascular computed tomographyDeep learningMachine learning

Identifiers

PMID33812855
PMCPMC8455701

What OpenQuestion holds

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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.