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Automated Vessel Occlusion Software in Acute Ischemic Stroke: Pearls and Pitfalls
Yasmin N Aziz1, Aakanksha Sriwastwa2, Kambiz Nael3
1Department of Neurology and Rehabilitation Medicine (Y.N.A., P.H., E.A.M., P.K.), University of Cincinnati, OH.
Stroke
|June 9, 2025
Summary
Artificial intelligence software for detecting vessel occlusions aids stroke triage. This review examines their utility, algorithms, real-world performance, and limitations for stroke care specialists.
Area of Science:
- Neurology
- Emergency Medicine
- Radiology
- Neurosurgery
Background:
- Artificial intelligence (AI) software is increasingly used for stroke triage to detect vessel occlusions.
- Information on the proprietary algorithms, user base, and real-world performance of these AI tools is limited.
- A multidisciplinary understanding is needed for effective implementation in stroke care.
Purpose of the Study:
- To provide an educational review of automated vessel occlusion (AVO) detection software.
- To discuss the emerging evidence, underlying algorithms, and diagnostic performance of AVO software.
- To offer practical guidance for the utilization of this technology in clinical settings.
Main Methods:
- Review of current literature and evidence on AI-based vessel occlusion detection.
- Analysis of the underlying algorithms and technological principles.
- Discussion of real-world diagnostic performance data and limitations.
Main Results:
- AI software shows emerging utility in aiding stroke triage.
- Real-world diagnostic performance varies, and limitations exist.
- Proprietary nature of software hinders transparent evaluation.
Conclusions:
- Automated vessel occlusion software offers potential benefits for stroke triage.
- Further unbiased research is needed to fully understand real-world efficacy and limitations.
- Multidisciplinary collaboration is key for successful integration and utilization of AI in stroke care.
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