From CCTA to Surgical Strategy: An Integrated AI Framework for Patient-Specific Coronary artery bypass grafting Planning

Background: Coronary artery bypass grafting (CABG) remains the standard of care for complex multivessel and left main coronary artery disease. However, current preoperative planning remains largely subjective, relying on qualitative interpretation of coronary CT angiography (CCTA), operator-dependent stenosis grading, and fragmented multi-software workflows. Invasive fractional flow reserve (FFR), the reference standard for physiologic lesion assessment, is infrequently acquired preoperatively, leaving distal anastomosis planning without an objective hemodynamic basis. Methods: We developed a fully automated, AI-powered platform that converts routine CCTA into a patient-specific CABG planning workflow through five integrated modules: nnU-Net based segmentation of coronary lumen and calcification; quantitative morphological and topological characterization generating more than thirty descriptors; automated stenosis detection using a local reference-radius formulation; a nine-point composite scoring framework for distal anastomosis site selection incorporating luminal caliber, landing-zone length, calcification burden, distal perfusion reserve, and bifurcation proximity; and interactive virtual graft construction coupled to a distributed reduced-order solver for pre- and post-bypass FFR estimation. Results: Lumen segmentation achieved a mean Dice similarity coefficient of 0.96 {+/-} 0.01, whereas calcium segmentation achieved 0.73 {+/-} 0.15 on the held-out cohort. Platform-derived FFR demonstrated strong agreement with invasively measured FFR (r=0.96, mean absolute relative difference 1.73 {+/-}1.42%) across the evaluated lesions, supporting the physiologic validity of the reduced-order hemodynamic solver. End-to-end analysis from raw CCTA to hemodynamic assessment and virtual graft planning was completed in approximately seven minutes per case on a standard workstation, representing a substantial reduction in processing time compared with conventional multi-tool and CFD-based workflows. Conclusions: The proposed platform demonstrates the feasibility of rapid, reproducible, and physiology-informed CABG planning using routine CCTA. By integrating anatomical characterization, automated target-site analysis, virtual graft construction, and reduced-order hemodynamic assessment into a single workflow, the framework provides objective, quantitative surgical decision support compatible with routine clinical workflows. Keywords: Coronary artery bypass grafting (CABG); Fractional flow reserve (FFR); Coronary CT angiography (CCTA); Surgical planning