Development and Initial Validation of the Novel Computational Method for Dynamic Intracardiac Blood Flow Evaluation

Background/Objectives: To develop and preliminarily evaluate a practical Python‑based program for universal dynamic intracardiac blood flow visualization and the extraction of new quantitative parameters, serving as an initial step toward future flow‑based cardiac evaluation. Methods: The method was applied across five imaging modalities (angiography, MRI, ICE, TEE, TTE) using standard diagnostic hardware. Preliminary testing used ECG‑gated ICE DICOM images from sixteen patients undergoing first‑time AF ablation. Results: The program produced straightforward, easily adoptable flow visualizations and automatically computed the *Turbulence Index (TI)* and *Blood Mobility Fraction (BMF)* across cardiac cycles. Distinct preliminary flow patterns were observed between sinus rhythm and atrial fibrillation. Outputs are exportable for AI analysis. Conclusions: This approach demonstrates preliminary feasibility for simple, broadly applicable intracardiac flow assessment and introduces TI and BMF as promising flow‑based biomarkers for future prognostic use.