Interpretable Machine Learning Model for Characterizing Hemodynamic Biomarkers in Bicuspid Aortic Valve induced Aortic Dilation

Motivation: This research is driven by improving risk stratification and managing aortic dilation in bicuspid aortic valve patients. Goal(s): The study seeks to identify predictive hemodynamic biomarkers from 4D flow cardiovascular magnetic resonance imaging correlating with aortic dilation in bicuspid aortic valve patients, improving risk assessment and clinical management. Approach: The study analyzed 4D flow MRI data using machine learning, precisely Random Forest and SHAP values, to identify hemodynamic biomarkers linked to aortic dilation. Results: The study identified twelve significant hemodynamic biomarkers, with the model achieving 89.57% accuracy in detecting aortic dilation in patients with bicuspid aortic valve. Impact: This study advances the understanding of hemodynamic changes in bicuspid aortic valve patients, offering clinically relevant biomarkers for aortic dilation. These insights could significantly enhance risk assessment, guiding tailored interventions and improving patient outcomes in cardiovascular care and management.