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Related Experiment Video

Updated: Aug 20, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
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Machine-learning algorithm in acute stroke: real-world experience.

N Chan1, N Sibtain2, T Booth3

  • 1Department of Neuroradiology, King's College Hospital, London, UK; Department of Interventional Neuroradiology, The Royal London Hospital, London, UK.

Clinical Radiology
|November 21, 2022
PubMed
Summary
This summary is machine-generated.

This study evaluated a machine learning (ML) algorithm for acute stroke. While the ML algorithm showed promise for detecting large vessel occlusions (LVO), its performance in identifying acute ischemic changes was limited by false positives.

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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Acute stroke diagnosis relies on rapid and accurate interpretation of neuroimaging.
  • Machine learning (ML) algorithms offer potential for automating and improving diagnostic accuracy in stroke assessment.
  • Evaluating the clinical performance of commercially available ML tools is crucial for their adoption in clinical practice.

Purpose of the Study:

  • To assess the clinical performance of a commercial machine learning (ML) algorithm (RAPID™) in the diagnosis of acute stroke.
  • To evaluate the accuracy of RAPID™ ASPECTS and RAPID™ CTA in detecting acute ischemic changes and large vessel occlusions (LVO).

Main Methods:

  • Retrospective analysis of CT and CT angiography (CTA) studies from 104 patients with suspected acute stroke.
  • Independent blinded review by two neuroradiologists.
  • Comparison of neuroradiologists' interpretations with real-time ML software analysis (RAPID™ ASPECTS and CTA).

Main Results:

  • RAPID™ ASPECTS demonstrated high sensitivity (87.5%) but poor specificity (30.9%) for acute ischemic changes, with a high false positive rate (51.1%).
  • RAPID™ CTA showed high sensitivity (92.3%) and specificity (85.3%) for LVO detection, with a moderate positive predictive value (52.2%).
  • RAPID™ ASPECTS correlated well with neuroradiologists' interpretations for proven LVO (Pearson correlation coefficient up to 0.96).

Conclusions:

  • RAPID™ CTA is a robust tool for detecting large vessel occlusions (LVO) in acute stroke.
  • RAPID™ ASPECTS' high false positive rate for ischemic changes may reduce clinician confidence, despite good performance in LVO cases.
  • The ML algorithm shows potential but requires further refinement for comprehensive acute stroke assessment.