Advanced computer analysis of pre-procedural CT scans accurately predicted adverse left ventricular remodeling in 232 TAVR patients, with radiomic texture analysis achieving 84% accuracy compared to just 62% for standard geometric measurements. The best-performing model combined CT radiomics with clinical factors like pre-procedural left ventricular mass index and residual valve insufficiency, reaching 86% accuracy. This represents a significant advancement in cardiac risk stratification for one of the fastest-growing cardiovascular procedures. TAVR has revolutionized treatment for severe aortic stenosis, particularly in elderly patients, but up to 22% experience adverse heart muscle changes that impair recovery. Current imaging methods poorly identify these at-risk patients before surgery. The radiomic approach extracts microscopic tissue texture patterns invisible to the human eye, potentially revealing early signs of myocardial dysfunction. However, this preprint awaits peer review, and the retrospective single-center design limits generalizability. The findings could transform pre-surgical planning by enabling personalized risk assessment and targeted interventions. Prospective multicenter validation will be essential before clinical implementation, but this work demonstrates promising potential for AI-enhanced cardiovascular medicine.