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

Anxiety-induced motion artifacts frequently compromise image quality during non-sedated cardiac magnetic resonance imaging (CMR), leading to rescans and increased healthcare burdens. This retrospective cohort study hypothesized that combining multidimensional psychological and physiological indicators could accurately predict artifact occurrence. Baseline data from 514 adult patients, encompassing 15 clinical features, were systematically analyzed. Four machine learning algorithms were trained and evaluated using the area under the receiver operating characteristic curve (AUC). The eXtreme Gradient Boosting model demonstrated the highest discriminative ability (AUC = 0.875). To provide clinical transparency, SHapley Additive exPlanations (SHAP) analysis was applied, revealing that state anxiety (evaluated via the State-Trait Anxiety Inventory State subscale, STAI-State), maximum breath-hold duration, and heart rate variability were primarily associated with artifact risk. The novelty of this approach lies in uncovering non-linear threshold effects-such as a sharp risk escalation when STAI-State scores exceeded 45-and complex interactions between physical capacity and claustrophobia. In conclusion, this interpretable machine learning framework offers a robust tool for patient-specific risk stratification, suggesting that preemptive and targeted nursing interventions could be optimized based on individualized algorithmic predictions.