Evidence map›Paper›PMID 37492680›Full record

ReviewFrontiers in radiology2022

Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes.

Minh B Nguyen, Olivier Villemain, Mark K Friedberg, Lasse Lovstakken, Craig G Rusin, Luc Mertens

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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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

6 authors.

Minh B NguyenDivision of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
Olivier VillemainDivision of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
Mark K FriedbergDivision of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
Lasse LovstakkenCentre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
Craig G RusinDepartment of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States.
Luc MertensDivision of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.

Indexed as

artificial intelligenceechocardiographypediatric cardiologypediatric echocardiographypediatrics

Identifiers

PMID37492680
PMCPMC10365116

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.