Artificial intelligence for the detection of large vessel occlusion stroke and workflow optimization: a systematic review and meta-analysis

To evaluate the impact of artificial intelligence (AI) tools on workflow efficiency, treatment times, and clinical outcomes in patients with large vessel occlusion (LVO) stroke.

We included observational studies that assessed the implementation of AI-based tools in the management of patients with suspected or confirmed LVO stroke. Eligible studies reported workflow, treatment, safety, or functional outcomes. Data were extracted on thrombolysis rates, CTA-to-arterial puncture time, door-to-puncture time, endovascular treatment (EVT) access, reperfusion success, symptomatic intracerebral hemorrhage (sICH), mortality, and functional independence (mRS 0-2) at 90 days. Random-effects models were applied to calculate pooled mean differences (MD) or risk ratios (RR) with 95% confidence intervals (CIs).

Thirteen studies were included. AI implementation significantly reduced workflow delays: CTA-to-puncture time decreased by -40.20 minutes (95% CI -66.41 to -13.99; P<0.01) and door-to-puncture time by -23.17 minutes (95% CI -38.71 to -7.63; P<0.01). Thrombolysis administration showed no difference (RR 0.79; 95% CI 0.57-1.09; P=0.15). Similarly, no significant effects were observed for EVT access (RR 0.97; 95% CI 0.66-1.44; P=0.89), reperfusion success (RR 1.06; 95% CI 1.00-1.12; P=0.06), sICH (RR 0.63; 95% CI 0.30-1.35; P=0.23), or mortality (RR 0.72; 95% CI 0.38-1.37; P=0.32). However, AI-based workflows significantly improved functional independence at 90 days (RR 1.55; 95% CI 1.12-2.15; P=0.008).

AI tools significantly improve treatment times and functional independence in acute LVO stroke without increasing adverse events.