Introduction and validation of OSCAR-optimal stent choice algorithm

PURPOSE: Standardization and international guidelines for stent size selection are lacking. In this study, we introduce and validate an artificial intelligence (AI)- and image processing-supported, modular software algorithm trained on a multicentric vascular segmentation dataset that identifies stenoses, performs segmentations of stenotic vessel segments and suggests the optimal stent size for implantation.

MATERIAL AND METHODS: This retrospective multicenter study included 149 patients who underwent stent implantation for symptomatic stenoses of the common and external iliac arteries between August 2017 and July 2024. Peri-interventional angiography datasets were evaluated by four board-certified interventional radiologists. For AI-training, all relevant stenoses were annotated and segmented to reflect intended stent sizing. The segmentation criteria were consensus-defined, and all readers completed a prior training session to ensure consistency. The modular algorithm comprises components for stenosis detection, segmentation and stent parameter prediction. Following pre-training on a publicly available coronary artery dataset, the model was fine-tuned on the study-specific iliac artery dataset using leave-one-out cross-validation.

RESULTS: OSCAR detected stenoses in 84.6% of cases. The model achieved a high recall (0.89 ± 0.21), meaning that most expert-annotated stenoses were correctly identified, while a moderate precision (0.65 ± 0.28) indicated some false-positive detections. Segmentation accuracy was good (DSC 0.77 ± 0.11). Stent diameter and length predictions demonstrated mean absolute percentage errors of 0.13 ± 0.18 and 0.33 ± 0.31, respectively, comparable to expert variability.

CONCLUSIONS: This proof-of-concept study demonstrates the potential of AI-assisted stent selection in vascular interventions. Furthermore, the option of a closed-loop framework promotes sustainability, reproducibility and cost-effectiveness in stent implantation procedures.