<p class="jove_content">This study develops an interpretable machine learning framework to predict anxiety-induced motion artifacts during non-sedated cardiac magnetic resonance imaging. The goal is to provide individualized risk stratification to guide targeted nursing interventions and optimize preemptive resource allocation.</p>

machine-learning-post-hoc-feature-attribution-for-predicting-cardiac

Machine Learning and Post-Hoc Feature Attribution for Predicting Cardiac MRI Motion Artifacts: A Retrospective Cohort Study

Anxiety-induced motion artifacts frequently compromise image quality during non-sedated cardiac magnetic resonance imaging (CMR), leading to rescans and ...

Institution - The Third People's Hospital of Chengdu

Articles Published in JoVE