Automated Collateral Flow Assessment in Patients with Acute Ischemic Stroke Using Computed Tomography with Artificial Intelligence Algorithms

Background: Collateral circulation is associated with improved functional outcome in patients with large vessel occlusion acute ischemic stroke (AIS) who undergo reperfusion therapy. Assessment of collateral flow can be time consuming, subjective, and difficult because of complex neurovasculature. This study assessed the ability of multiple artificial intelligence algorithms in determining collateral flow of patients with AIS.

Methods: Two hundred patients with AIS between March 2019 and January 2020 were included in this retrospective study. Peak arterial computed tomography perfusion volumes were used to assess collateral scores. Neural networks were developed for dichotomized (≥50% or <50%) and multiclass (0% filling, 0%-50% filling, 50%-100% filling, or 100% filling) collateral scoring. Maximum intensity projections from axial and anteroposterior (AP) views were synthesized for each bone subtracted three-dimensional volume and used as network inputs separately and together, along with three-dimensional data. Training:testing:validation splits of 60:30:10 and 20 iterations of Monte Carlo cross-validation were used. Network performance was assessed using 95% confidence intervals of accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results: The axial and AP input combination provided the most accurate results for dichotomized classification: accuracy, 0.85 ± 0.01; sensitivity, 0.88 ± 0.02; specificity, 0.82 ± 0.03; PPV, 0.86 ± 0.02; and NPV, 0.83 ± 0.03. Similarly, the axial and AP input combination provided the best results for multiclass classification: accuracy, 0.80 ± 0.01; sensitivity, 0.64 ± 0.01; specificity, 0.85 ± 0.01; PPV, 0.65 ± 0.02; and NPV, 0.85 ± 0.01.

Conclusions: This study reports one of the first artificial intelligence-based algorithms capable of accurately and efficiently assessing collateral flow of patients with AIS. This automated method for determining collateral filling could streamline clinical workflow, reduce bias, and aid in clinical decision making for determining reperfusion-eligible patients.