Researchers developed automated AI-based indices that integrate right atrial and ventricular strain measurements to assess tricuspid regurgitation severity and patient prognosis. In 8,231 patients followed for 19 months, the combined remodeling indices outperformed traditional individual measurements for identifying severe disease, with area under the curve improvements from 0.757 to 0.857 for atrial assessment. The right ventricular remodeling index emerged as the stronger independent predictor, showing a 2.32-fold increased risk per unit decrease and providing incremental prognostic value beyond standard measures. This automated approach represents a significant advancement in cardiovascular risk assessment, moving beyond valve-centric evaluations to comprehensive right-heart phenotyping. The ability to automatically classify patients into four distinct remodeling patterns could transform clinical decision-making, enabling earlier intervention in high-risk individuals. However, this preprint awaits peer review, and the 19-month follow-up period is relatively short for cardiovascular outcomes research. The technology's dependence on AI algorithms also raises questions about generalizability across different populations and imaging systems. While promising for precision cardiology, longer-term validation studies will be essential before clinical implementation.