A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac
Markus Huellebrand1,2, Matthias Ivantsits1, Lennart Tautz2
1Institute of Cardiovascular Computer-Assisted Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Frontiers in Cardiovascular Medicine
|April 1, 2022
Summary
A new software infrastructure enhances machine learning (ML) model training for cardiovascular data interpretation. This tool improves model performance and helps clinicians understand complex cardiac MRI data features.
Area of Science:
- Cardiovascular imaging and machine learning.
- Medical data interpretation and analysis.
- Clinical decision support systems.
Background:
- Machine learning (ML) acceptance in cardiovascular data interpretation hinges on model design, training, and clinical expert collaboration.
- Effective ML models require robust software infrastructure for iterative training, evaluation, and feature understanding.
- Multimodal data interpretation, particularly with cardiac MRI, presents unique challenges for ML model development.
Purpose of the Study:
- To introduce a software infrastructure designed to enhance the training and application of ML models for cardiovascular data interpretation.
- To support iterative refinement of ML models through interactive visual analytics for data correction, annotation, and result exploration.
- To facilitate clinical expert understanding of classification-relevant data features in cardiac MRI.
Main Methods:
- Development of a software infrastructure supporting iterative ML model training and evaluation.
- Integration of interactive visual analytics tools for data curation, annotation, and result interpretation.
- Application of pre-trained 2D U-Nets for segmentation and radiomics-based classifiers for diagnostic tasks on cardiac MRI data from ACDC and EMIDEC challenges.
Main Results:
- The software successfully identified outliers in automatic segmentation and cardiac MRI acquisition.
- Targeted curation and expert annotations demonstrably improved ML model performance.
- Clinical experts gained insights into anatomical and functional characteristics linked to disease classes.
Conclusions:
- The developed software infrastructure effectively supports the iterative training and application of ML models in cardiovascular data interpretation.
- Interactive visual analytics tools enhance clinical expert engagement and understanding of ML model outputs.
- Improvements in data quality and expert annotation lead to enhanced ML model performance for cardiac diagnostics.
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