Abstract
OBJECTIVE: Artificial intelligence (AI) holds promise for advancing neuroendovascular therapy through device evaluation, but its application in real-world clinical settings remains limited. We aimed to validate the feasibility of AI-based quantitative device evaluation during actual procedures by assessing the stability of the Rist radial access guide catheter (Medtronic, Dublin, Ireland), a novel device designed for the increasingly adopted transradial approach (TRA), during flow diverter stent (FDS) placement. METHODS: We retrospectively analyzed 4 cases of FDS placement using Rist via the TRA. Rist was tracked in recorded fluoroscopic videos using the AI technology of Neuro-Vascular Assist (iMed Technologies, Tokyo, Japan). The movement distance of Rist during FDS placement was calculated as a stability indicator. RESULTS: All procedures were successfully completed without any complications. Rist was introduced from the radial artery and positioned in the distal internal carotid artery. The maximum movement distances of the Rist during the procedures were 3.36, 6.63, 1.79, and 0.33 mm for each case, respectively, with an average of 3.03 mm. The maximum movement distances per minute were 1.68, 2.34, 1.19, and 0.46 mm/min, respectively, with a mean of 1.42 mm/min. These measurements suggest sufficient stability for the FDS procedures. CONCLUSION: This study demonstrates the feasibility of using AI technology to quantitatively analyze Rist stability in TRA procedures. To the best of our knowledge, this is the 1st clinical evaluation of device function in a clinical setting using AI technology. Further studies with more cases are required to validate these findings. This method is promising for real-world device evaluation and development.