Evidence map›Paper›PMID 39194982›Full record

ArticleJournal of imaging2024

Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI.

Ali Moradi, Olawale O Olanisa, Tochukwu Nzeako, Mehregan Shahrokhi, Eman Esfahani, Nastaran Fakher, Mohamad Amin Khazeei Tabari

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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.

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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

7 authors.

Ali MoradiInternal Medicine, HCA Florida, Blake Hospital, Morsani College of Medicine, University of South Florida, Bradenton, FL 34209, USA.
Olawale O OlanisaInternal Medicine, Adjunct Clinical Faculty, Michigan State University College of Human Medicine, Trinity Health Grand Rapids, Grand Rapids, MI 49503, USA.ORCID 0009-0007-8117-2486
Tochukwu NzeakoInternal Medicine, Christiana Care Hospital, Newark, DE 19718, USA.
Mehregan ShahrokhiSchool of Medicine, Shiraz University of Medical Sciences, Shiraz 71348-45794, Iran.ORCID 0000-0002-2901-9712
Eman EsfahaniFaculty of Medicine, Semmelweis University, 1085 Budapest, Hungary.
Nastaran FakherFaculty of Medicine, Semmelweis University, 1085 Budapest, Hungary.ORCID 0009-0004-7475-2245
Mohamad Amin Khazeei TabariStudent Research Committee, Mazandaran University of Medical Sciences, Sari 48175-866, Iran.ORCID 0000-0001-6415-8403

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

introductionCardiac imaging is crucial for diagnosing heart disorders. Methods like X-rays, ultrasounds, CT scans, and MRIs provide detailed anatomical and functional heart images. AI can enhance these imaging techniques with its advanced learning capabilities.

methodIn this scoping review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Guidelines, we searched PubMed, Scopus, Web of Science, and Google Scholar using related keywords on 16 April 2024. From 3679 articles, we first screened titles and abstracts based on the initial inclusion criteria and then screened the full texts. The authors made the final selections collaboratively.

resultThe PRISMA chart shows that 3516 articles were initially selected for evaluation after removing duplicates. Upon reviewing titles, abstracts, and quality, 24 articles were deemed eligible for the review. The findings indicate that AI enhances image quality, speeds up imaging processes, and reduces radiation exposure with sensitivity and specificity comparable to or exceeding those of qualified radiologists or cardiologists. Further research is needed to assess AI's applicability in various types of cardiac imaging, especially in rural hospitals where access to medical doctors is limited.

conclusionsAI improves image quality, reduces human errors and radiation exposure, and can predict cardiac events with acceptable sensitivity and specificity.

Indexed as

artificial intelligencecardiac imagingechocardiographymagnetic resonance imaging

Identifiers

PMID39194982
PMCPMC11355719

What Socratic 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 Socratic graph.