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Purpose of Review <p>Artificial intelligence (AI) is increasingly being integrated into neuroimaging workflows for the evaluation of acute ischemic stroke (AIS). This review examines the current role of AI-based imaging tools in AIS care, focusing on their use in automated detection of imaging abnormalities and emerging applications that provide quantitative imaging biomarkers to support treatment decision-making.</p> Recent Findings <p>Several commercially available AI platforms can assist with rapid detection of intracranial hemorrhage, large vessel occlusion (LVO), and early ischemic change on CT and CT angiography imaging. Observational studies suggest that implementation of these systems may improve workflow efficiency and reduce time to treatment within acute stroke pathways. However, evidence demonstrating consistent improvement in long-term functional outcomes remains limited. Recent research has expanded the role of AI beyond abnormality detection toward quantitative analysis of infarct burden, vascular anatomy, thrombus characteristics, and patient-level biomarkers such as sarcopenia derived from routine imaging.</p> Summary <p>AI-assisted neuroimaging has the potential to enhance imaging-guided decision-making in AIS by combining rapid abnormality detection with reproducible quantification of clinically relevant imaging biomarkers. Continued validation and careful integration into existing stroke workflows will be necessary to ensure safe, effective, and equitable implementation in clinical practice.</p> Opinion Statement <p>AI is increasingly being integrated into neuroimaging workflows for the evaluation of AIS. Current commercially available AI platforms primarily assist with rapid detection of imaging abnormalities such as intracranial hemorrhage, large vessel occlusion, and early ischemic change, and may improve workflow efficiency and reduce time to treatment in acute stroke pathways. However, evidence demonstrating consistent improvement in long-term functional outcomes remains limited, and much of the available evidence is derived from single-center observational studies with heterogeneous study designs. Beyond detection, emerging AI approaches are enabling quantitative characterization of infarct burden, vascular anatomy, thrombus morphology, and patient-level biomarkers such as sarcopenia or frailty derived from routine imaging. These imaging-derived quantitative features may provide additional information to support treatment selection and procedural planning, particularly in patients with borderline eligibility for reperfusion therapies. As AI tools continue to evolve, their greatest clinical value may lie in combining rapid detection with reproducible quantification of imaging biomarkers relevant to imaging-guided decision-making in AIS. Careful validation and integration into existing stroke workflows will be essential to ensure safe and effective implementation.</p>

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The Use of AI in Neuroimaging-guided Decision Making in Acute Ischemic Stroke

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Kazbek Barakhanov,
  • Taha Aslan,
  • Kaue T N Duarte,
  • Aravind Ganesh

摘要

Purpose of Review

Artificial intelligence (AI) is increasingly being integrated into neuroimaging workflows for the evaluation of acute ischemic stroke (AIS). This review examines the current role of AI-based imaging tools in AIS care, focusing on their use in automated detection of imaging abnormalities and emerging applications that provide quantitative imaging biomarkers to support treatment decision-making.

Recent Findings

Several commercially available AI platforms can assist with rapid detection of intracranial hemorrhage, large vessel occlusion (LVO), and early ischemic change on CT and CT angiography imaging. Observational studies suggest that implementation of these systems may improve workflow efficiency and reduce time to treatment within acute stroke pathways. However, evidence demonstrating consistent improvement in long-term functional outcomes remains limited. Recent research has expanded the role of AI beyond abnormality detection toward quantitative analysis of infarct burden, vascular anatomy, thrombus characteristics, and patient-level biomarkers such as sarcopenia derived from routine imaging.

Summary

AI-assisted neuroimaging has the potential to enhance imaging-guided decision-making in AIS by combining rapid abnormality detection with reproducible quantification of clinically relevant imaging biomarkers. Continued validation and careful integration into existing stroke workflows will be necessary to ensure safe, effective, and equitable implementation in clinical practice.

Opinion Statement

AI is increasingly being integrated into neuroimaging workflows for the evaluation of AIS. Current commercially available AI platforms primarily assist with rapid detection of imaging abnormalities such as intracranial hemorrhage, large vessel occlusion, and early ischemic change, and may improve workflow efficiency and reduce time to treatment in acute stroke pathways. However, evidence demonstrating consistent improvement in long-term functional outcomes remains limited, and much of the available evidence is derived from single-center observational studies with heterogeneous study designs. Beyond detection, emerging AI approaches are enabling quantitative characterization of infarct burden, vascular anatomy, thrombus morphology, and patient-level biomarkers such as sarcopenia or frailty derived from routine imaging. These imaging-derived quantitative features may provide additional information to support treatment selection and procedural planning, particularly in patients with borderline eligibility for reperfusion therapies. As AI tools continue to evolve, their greatest clinical value may lie in combining rapid detection with reproducible quantification of imaging biomarkers relevant to imaging-guided decision-making in AIS. Careful validation and integration into existing stroke workflows will be essential to ensure safe and effective implementation.