Machine Learning–Enabled Rupture Risk Prediction in Aortic Aneurysms Using Imaging Biomarkers and Clinical Profiles

Aortic aneurysm rupture is a life-threatening event associated with high mortality, often occurring without warning despite routine surveillance. Current clinical decision-making relies heavily on aneurysm diameter and growth rate, which fail to capture the complex biomechanical and biological factors contributing to rupture risk. Advances in medical imaging and machine learning (ML) provide an opportunity to move beyond size-based criteria toward personalized, data-driven risk prediction. This paper presents a conceptual framework for machine learning–enabled rupture risk prediction in aortic aneurysms by integrating imaging-derived biomarkers with patient-specific clinical profiles. Imaging features such as aneurysm morphology, wall stress indicators, thrombus characteristics, and texture-based radiomics are combined with demographic, physiological, and comorbidity data to support individualized risk stratification. The study positions ML not as a replacement for clinical judgment but as an intelligent decision-support mechanism that enhances early detection, surveillance planning, and intervention timing. The framework provides a foundation for future empirical validation and clinical translation in vascular medicine.