Evidence map›Paper›PMID 38229001›Full record

ArticleJournal of cardiovascular translational research2024

Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images.

Glisant Plasa, Elizabeth Hillier, Judy Luu, Dominic Boutet, Mitchel Benovoy, Matthias G Friedrich

Abstract read
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In one paragraph

Article in Journal of cardiovascular translational research, 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. 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

6 authors.

Glisant PlasaResearch Institute of the McGill University Health Centre, Montreal, Canada.
Elizabeth HillierResearch Institute of the McGill University Health Centre, Montreal, Canada.
Judy LuuResearch Institute of the McGill University Health Centre, Montreal, Canada.
Dominic BoutetFaculty of Science, Neuroscience, McGill University, Montreal, Canada.
Mitchel BenovoyResearch Institute of the McGill University Health Centre, Montreal, Canada.
Matthias G FriedrichResearch Institute of the McGill University Health Centre, Montreal, Canada. mgwfriedrich@gmail.com.ORCID 0000-0003-1204-5647

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oxygenation-sensitive cardiovascular magnetic resonance (OS-CMR) is a novel, powerful tool for assessing coronary function in vivo. The data extraction and analysis however are labor-intensive. The objective of this study was to provide an automated approach for the extraction, visualization, and biomarker selection of OS-CMR images. We created a Python-based tool to automate extraction and export of raw patient data, featuring 3336 attributes per participant, into a template compatible with common data analytics frameworks, including the functionality to select predictive features for the given disease state. Each analysis was completed in about 2 min. The features selected by both ANOVA and MIC significantly outperformed (p < 0.001) the null set and complete set of features in two datasets, with mean AUROC scores of 0.89eatures f 0.94lete set of features in two datasets, with mean AUROC scores that our tool is suitable for automated data extraction and analysis of OS-CMR images.

Indexed as

Image Interpretation, Computer-AssistedPredictive Value of TestsAutomationCoronary Artery DiseaseCoronary VesselsDatabases, FactualData MiningHumansMagnetic Resonance ImagingMaleOxygenReproducibility of ResultsOxygenCoronary vascular functionMachine learning for medical imagingMagnetic resonance imagingOxygenation-sensitive cardiovascular magnetic resonance

Identifiers

PMID38229001

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.