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39 reviewsSummaryeClinicalMedicine2025;82: 103186Background Accurate identification of high-risk vulnerable plaques and assessment of stroke risk are crucial forPublished Online 3 Aprilclinical decision-making, yet reliable non-invasive predictive tools are currently lacking. This study aimed to2025develop an artificial intelligence model based on high-resolution vessel wall imaging (HR-VWI) to assist in the of vulnerable plaques and prediction of stroke recurrence risk in patients with symptomatic1016/j.eclinm.2025.intracranial atherosclerotic stenosis (sICAS).103186Methods Between June 2018 and June 2024, a retrospective collection of HR-VWI images from 1806 plaques in 726sICAS patients across four medical institutions was conducted. K-means clustering was applied to the T1-weightedimaging (T1WI) and T1-weighted imaging with contrast enhancement (T1CE) sequences. Following featureextraction and selection, radiomic models and habitat models were constructed. Additionally, the VisionTransformer (ViT) architecture was utilized for HR-VWI image analysis to build a deep learning model. Astacking fusion strategy was employed to integrate the habitat model and ViT model, enabling effectiveidentification of high-risk vulnerable plaques in the intracranial region and prediction of stroke recurrence risk.Model performance was evaluated using receiver operating characteristic (ROC) curves, and model comparisonswere conducted using the DeLong test. Furthermore, decision curve analysis and calibration curves were utilizedto assess the practicality and clinical value of the model.