Novel artificial intelligence approach in neurointerventional practice: Preliminary findings on filter movement and ischemic lesions in carotid artery stenting

Abstract

Background and objectives

Embolic protection devices (EPDs) used during carotid artery stenting (CAS) are crucial in reducing ischemic complications. Although minimizing the filter-type EPD movement is considered important, limited research has demonstrated this practice. We used an artificial intelligence (AI)-based device recognition technology to investigate the correlation between filter movements and ischemic complications.

Methods

We retrospectively studied 28 consecutive patients who underwent CAS using FilterWire EZ (Boston Scientific, Marlborough, MA, USA) from April 2022 to September 2023. Clinical data, procedural videos, and postoperative magnetic resonance imaging were collected. An AI-based device detection function in the Neuro-Vascular Assist (iMed Technologies, Tokyo, Japan) was used to quantify the filter movement. Multivariate proportional odds model analysis was performed to explore the correlations between postoperative diffusion-weighted imaging (DWI) hyperintense lesions and potential ischemic risk factors, including filter movement.

Results

In total, 23 patients had sufficient information and were eligible for quantitative analysis. Fourteen patients (60.9 %) showed postoperative DWI hyperintense lesions. Multivariate analysis revealed significant associations between filter movement distance (odds ratio, 1.01; 95 % confidence interval, 1.00–1.02; p = 0.003) and high-intensity signals in time-of-flight magnetic resonance angiography with DWI hyperintense lesions. Age, symptomatic status, and operative time were not significantly correlated.

Conclusion

Increased filter movement during CAS was correlated with a higher incidence of postoperative DWI hyperintense lesions. AI-based quantitative evaluation of endovascular techniques may enable demonstration of previously unproven recommendations. To the best of our knowledge, this is the first study to use an AI system for quantitative evaluation to address real-world clinical issues.