Development and Clinical Validation of a Stroke Risk Decision System for Patients With Carotid Artery Plaque Based on AI ‐Powered Handheld Ultrasound and Laboratory Markers

OBJECTIVE: This study aimed to develop and validate a fusion model combining portable artificial intelligence (AI) ultrasound imaging with multidimensional laboratory data to stratify risk and support decision-making in patients with carotid plaque-related anterior circulation stroke, and to assess its predictive performance and clinical utility. METHODS: This prospective study enrolled 639 patients with carotid plaque via AI-powered ultrasound at community health centers. Anterior circulation stroke (ACS)/transient ischemic attack (TIA) patients were excluded. All participants completed the 180-day follow-up. The primary outcome was new-onset anterior circulation stroke or TIA, defined as focal neurological deficit > 24 h with CT/MRI-confirmed infarction, or transient (< 24 h) deficit without evidence of acute infarction on neuroimaging. Outcomes were adjudicated by two neurologists (attending or above), with a third for disagreements. Participants were classified into ACS group (n = 140) and non-ACS group (n = 499). Ultrasound features of carotid plaques and clinical laboratory data were compared between the two groups. Multivariable logistic regression was performed to identify factors associated with ACS, and a nomogram was constructed based on the regression model. Internal validation was conducted using the bootstrap method. Model performance was assessed in terms of discrimination (C-index and area under the curve [AUC]), calibration (calibration curves), and clinical utility (decision curve analysis [DCA] and clinical impact curves [CIC]). RESULTS: (1) Significant differences were observed between the two groups in history of hypertension, smoking, hyperlipidemia, stroke, and myocardial infarction (MI), as well as plasma triglyceride (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL) levels, carotid plaque thickness, plaque area, plaque echogenicity percentage, degree of carotid stenosis, plaque continuity score, and plaque mutability score (p < 0.05). No significant differences were found in sex, history of diabetes, total cholesterol (TC), plaque location, or plaque fibrous cap continuity analysis score (p > 0.05) (2) Multivariable logistic regression identified history of stroke, history of myocardial infarction, plaque thickness, LDL, and HDL as independent predictors of stroke. A nomogram based on these factors demonstrated good discrimination (C-index/AUC = 0.884), with a sensitivity of 84.29% and specificity of 72.55%, along with excellent calibration (Hosmer-Lemeshow test p = 0.250). Decision curve analysis showed superior net benefit across a range of risk thresholds compared with the null model, supporting a multi-threshold stratified intervention strategy. Notably, when the predicted risk exceeded 75%, the model's predictions closely aligned with observed outcomes. CONCLUSION: The stroke risk decision system developed using AI-powered handheld ultrasound and laboratory indicators demonstrates clinical applicability for early prediction of anterior circulation ischemic stroke and is particularly suitable for early screening and risk stratification of high-risk stroke populations in resource-limited settings.