Pilot clinical evaluation of Artificial Intelligence (AI)–driven guiding catheter simulation for optimizing percutaneous coronary intervention

Background In percutaneous coronary intervention (PCI), a suboptimal choice of guiding catheter may compromise coaxial alignment and backup support, prolonging procedures and increasing radiation and contrast exposure. We assessed whether a computed tomography–driven, artificial intelligence (AI)–guided preprocedural simulation could improve procedural efficiency and safety. Methods In a single-centre prospective registry with historical controls, fifty-five consecutive elective procedures performed with CT-based AI–assisted guiding-catheter selection were compared with fifty-five procedures performed without assistance. The primary endpoint was total procedure time from arterial access to completion. Secondary endpoints included time to coronary engagement, radiation dose, contrast volume, and guiding-catheter-related events. Results CT-based AI assistance was associated with shorter procedures (mean 68.5 vs 91.8 minutes), shorter engagement time, lower radiation dose, and lower contrast use. Guiding-catheter exchanges were fewer, and catheter-related events were lower (3.6% vs 16.4%; risk ratio 0.22; 95% confidence interval 0.05–0.98). Procedural success was one hundred percent in both groups with no in-hospital major adverse cardiac or cerebrovascular events. Conclusions A computed tomography–driven, CT-based AI–guided simulation for guiding-catheter selection was associated with greater procedural efficiency and a favourable profile in elective PCI. This approach, which standardises catheter choice and is associated with fewer empirical catherter exchanges, warrants confirmation in multicentre randomised studies and may help optimise resource utilisation in routine PCI. Trial registration Not applicable (observational registry).