Evidence map›Paper›PMID 31737539›Full record

ReviewCardiovascular diagnosis and therapy2019

Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need.

Arghavan Arafati, Peng Hu, J Paul Finn, Carsten Rickers, Andrew L Cheng, Hamid Jafarkhani, Arash Kheradvar

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cardiovascular diagnosis and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. The molecular mechanisms of cardiac development and related diseases.Signal transduction and targeted therapy · 2024
    Review
  5. Trustworthy and ethical AI-enabled cardiovascular care: a rapid review.BMC medical informatics and decision making · 2024
    Review
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  20. Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2020
    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

7 authors.

Arghavan ArafatiThe Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, USA.
Peng HuDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
J Paul FinnDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Carsten RickersUniversity Heart Center, Adult with Congenital Heart Disease Unit, University Hospital Hamburg-Eppendorf, Hamburg, Germany.
Andrew L ChengDepartment of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Hamid JafarkhaniCenter for Pervasive Communications and Computing, University of California, Irvine, CA, USA.
Arash KheradvarThe Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiac MRI (CMR) allows non-invasive, non-ionizing assessment of cardiac function and anatomy in patients with congenital heart disease (CHD). The utility of CMR as a non-invasive imaging tool for evaluation of CHD have been growing exponentially over the past decade. The algorithms based on artificial intelligence (AI), and in particular, deep learning, have rapidly become a methodology of choice for analyzing CMR. A wide range of applications for AI have been developed to tackle challenges in various aspects of CMR, and significant advances have also been made from image acquisition to image analysis and diagnosis. We include an overview of AI definitions, different architectures, and details on well-known methods. This paper reviews the major deep learning concepts used for analyses of patients with CHD. In the end, we have summarized a list of open challenges and concerns to be considered for future studies.

Indexed as

artificial intelligence (AI)Cardiac MRI (CMR)cardiac segmentationcongenital heart disease (CHD)deep learning

Identifiers

PMID31737539
PMCPMC6837938

What OpenQuestion holds

Textmetadata
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.