Pilot clinical evaluation of artificial intelligence–driven guiding catheter simulation for optimizing percutaneous coronary intervention

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 (CT)–driven, artificial intelligence (AI)–guided preprocedural simulation could improve procedural efficiency and safety.

In a single-centre prospective registry with historical controls, 55 consecutive elective procedures performed with CT-based AI-assisted guiding-catheter selection were compared with 55 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. Computed tomography–-based AI assistance was associated with shorter procedures (mean 68.5 vs. 91.8 min), 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 100% in both groups with no in-hospital major adverse cardiac or cerebrovascular events.

A CT-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 standardizes catheter choice and is associated with fewer empirical catheter exchanges, warrants confirmation in multicentre randomized studies and may help optimize resource utilization in routine PCI.