Why the study?
Machine learning may improve myocardial motion quantification robustness and identify motion patterns that differentiate pathologies rather than relying on pre-conceived models.
Machine learning strategies offer potential improvements in quantifying myocardial motion and identifying pathological deformation patterns.
May refine research quantification of myocardial motion; leaves open clinical translation pending prospective validation.
Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than pre-conceived models, machine learning could either improve the robustness of motion quantification or reveal patterns of motion and deformation (rather than single parameters) that differentiate pathologies. We review machine learning strategies for extracting motion-related descriptors and analyzing such features among populations, keeping in mind constraints specific to the cardiac application.
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Duchateau et al. (2020) studied this question.
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