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

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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.

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Data Availability

All python code for the toolkit can be made available upon request. A sample of the data can also be provided on demand.

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Funding

Parts of the work were funded by the MUHC McGill University Health Center Foundation.

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Authors and Affiliations

  1. Research Institute of the McGill University Health Centre, Montreal, Canada

    Glisant Plasa, Elizabeth Hillier, Judy Luu, Mitchel Benovoy & Matthias G. Friedrich

  2. Faculty of Science, Neuroscience, McGill University, Montreal, Canada

    Glisant Plasa & Dominic Boutet

  3. Area 19 Medical, Montreal, Canada

    Glisant Plasa & Mitchel Benovoy

  4. Department of Medicine and Health Sciences, Faculty of Medicine and Dentistry, McGill University, Montreal, Canada

    Elizabeth Hillier

  5. Department of Medicine and Diagnostic Radiology, McGill University Health Centre, 1001 Decarie Blvd, Montreal, QC, H4A 3J1, Canada

    Matthias G. Friedrich

Authors
  1. Glisant Plasa
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  2. Elizabeth Hillier
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  6. Matthias G. Friedrich
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Corresponding author

Correspondence to Matthias G. Friedrich.

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Ethics Approval

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5).

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Informed consent was obtained from all patients for being included in the study.

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The authors declare no competing interests.

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Editor-in-Chief Enrique Lara-Pezzi oversaw the review of this article.

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Plasa, G., Hillier, E., Luu, J. et al. Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images. J. of Cardiovasc. Trans. Res. (2024). https://doi.org/10.1007/s12265-023-10474-7

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  • DOI: https://doi.org/10.1007/s12265-023-10474-7

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