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16-01-2024 | Biomarkers | Original Article

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

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

Published in: Journal of Cardiovascular Translational Research | Issue 3/2024

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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.

Graphical Abstract

Appendix
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Metadata
Title
Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images
Authors
Glisant Plasa
Elizabeth Hillier
Judy Luu
Dominic Boutet
Mitchel Benovoy
Matthias G. Friedrich
Publication date
16-01-2024
Publisher
Springer US
Keyword
Biomarkers
Published in
Journal of Cardiovascular Translational Research / Issue 3/2024
Print ISSN: 1937-5387
Electronic ISSN: 1937-5395
DOI
https://doi.org/10.1007/s12265-023-10474-7

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