Purpose: This study aimed to evaluate the predictive value of automatically assessed collateral circulation (CC) and infarct core for functional outcomes in acute ischemic stroke (AIS) patients treated with endovascular thrombectomy (EVT).

Methods: We conducted a retrospective cohort study of 208 patients with anterior large vessel occlusion treated with EVT. Two AI-powered software were used to assess CC and infarct core. Comparative analyses included patient demographics, clinical and imaging data, and functional outcomes. Univariate and multivariable logistic regression analyses were conducted to predict 90-day functional outcomes, with a favorable outcome defined as a modified Rankin scale (mRS) score ≤2.

Results: Of the 208 patients, 114 (54.8%) were women, and the mean age was 71.4 ± 13.3 years. Patients with higher collateral scores (CS) had lower infarct core volumes (p < 0.001) and better mRS scores at 90 days (p = 0.008). In patients with a favorable outcome, the mean infarct core volume was lower than in those with poor outcomes (5 mL vs. 8.6 mL, p = 0.003). In univariate logistic regression, both infarct core (OR 0.94, p = 0.005) and CS (OR 1.84, p = 0.014) were predictors of favorable outcome. However, in multivariable models, only infarct core remained a significant independent predictor [AORs of 0.95 (p = 0.021) and 0.96 (p = 0.039)].

Conclusion: Automatically assessed infarct core is a robust predictor of functional outcome in AIS patients post-EVT, whereas CS’s predictive value diminishes when adjusted for infarct core. These findings support the integration of AI-powered evaluations in clinical settings to improve prognosis and treatment strategies for AIS