Evidence map›Paper›PMID 35919128›Full record

ReviewEuropean heart journal open2022

Machine learning applications in cardiac computed tomography: a composite systematic review.

Jonathan James Hyett Bray, Moghees Ahmad Hanif, Mohammad Alradhawi, Jacob Ibbetson, Surinder Singh Dosanjh, Sabrina Lucy Smith, Mahmood Ahmad, Dominic Pimenta

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European heart journal open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. 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

8 authors.

Jonathan James Hyett BrayInstitute of Life Sciences 2, Swansea University Medical, School, Swansea, UK.ORCID https://orcid.org/0000-0003-1167-6295
Moghees Ahmad HanifCardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK.
Mohammad AlradhawiUniversity College London Medical School, London WC1E 6DE, UK.
Jacob IbbetsonCardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK.
Surinder Singh DosanjhUniversity College London Medical School, London WC1E 6DE, UK.
Sabrina Lucy SmithBarts and the London School of Medicine and Dentistry, London E1 2AD, UK.
Mahmood AhmadCardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0001-9107-3704
Dominic PimentaRichmond Research Institute, St George's Hospital, University of London, Cranmer Terrace, Tooting, London SW17 0RE, UK.ORCID https://orcid.org/0000-0002-3179-7249

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

Indexed as

Artificial intelligenceCardiac computed tomographyMachine learning

Identifiers

PMID35919128
PMCPMC9242067

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.