Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images.
Glisant PlasaElizabeth HillierJudy LuuDominic BoutetMitchel BenovoyMatthias G FriedrichPublished in: Journal of cardiovascular translational research (2024)
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.
Keyphrases
- magnetic resonance
- deep learning
- big data
- electronic health record
- machine learning
- convolutional neural network
- optical coherence tomography
- artificial intelligence
- coronary artery disease
- coronary artery
- heart failure
- data analysis
- high throughput
- atrial fibrillation
- left ventricular
- high resolution
- blood flow
- contrast enhanced