Evidence map›Paper›PMID 39126604›Full record

ArticleThe international journal of cardiovascular imaging2024

Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

Jeffrey Yim, Mobina Mahdavi, Hooman Vaseli, Christina Luong, Michael Y C Tsang, Darwin F Yeung, Ken Gin, Marion E Barnes, Parvathy Nair, John Jue and 2 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in The international journal of cardiovascular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Jeffrey Yim *Division of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
Mobina Mahdavi *Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Hooman Vaseli *Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Christina LuongDivision of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
Michael Y C TsangDivision of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
Darwin F YeungDivision of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
Ken GinDivision of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
Marion E BarnesDivision of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
Parvathy NairDivision of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
John JueDivision of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
Purang Abolmaesumi *Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Teresa S M Tsang *Division of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada. t.tsang@ubc.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL) models. A total of 30,080 unique studies were included; 24,013 studies were used to train a convolutional neural network model to automatically assess, at end-diastole, LV internal diameter (LVID), interventricular septal wall thickness (IVS), posterior wall thickness (PWT), and LV mass. The model was trained to select end-diastolic frames with the largest LVID and to identify four landmarks, marking the dimensions of LVID, IVS, and PWT using manually labeled landmarks as reference. The model was validated with 3,014 echocardiographic cines and the accuracy of the model was evaluated with a test set of 3,053 echocardiographic cines. The model accurately measured LVID, IVS, PWT, and LV mass compared to study report values with a mean relative error of 5.40%, 11.73%, 12.76%, and 13.93%, respectively. The 𝑅

Indexed as

Deep LearningHeart VentriclesImage Interpretation, Computer-AssistedPredictive Value of TestsVentricular Function, LeftAgedAnatomic LandmarksEchocardiographyFemaleHumansMaleMiddle AgedModels, CardiovascularReproducibility of ResultsUncertaintyArtificial intelligenceEchocardiographyLeft ventricle.Machine learning

Identifiers

PMID39126604

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.