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Deep Learning Based Software to Identify Large Vessel Occlusion on Noncontrast Computed Tomography.

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A new machine learning algorithm, MethinksLVO, reliably identifies large vessel occlusion (LVO) on noncontrast computed tomography (NCCT) in acute stroke patients. This tool can expedite treatment decisions and improve patient transfer efficiency.

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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Large vessel occlusion (LVO) detection on noncontrast computed tomography (NCCT) is crucial for timely endovascular treatment in acute stroke.
  • Current methods may require advanced imaging like computed tomography angiography (CTA), potentially delaying treatment initiation.

Purpose of the Study:

  • To validate the accuracy of a novel machine learning algorithm (MethinksLVO) for identifying LVO directly from NCCT scans.
  • To assess the performance enhancement of the algorithm by incorporating clinical data (National Institutes of Health Stroke Scale and time from onset).

Main Methods:

  • A deep learning algorithm (MethinksLVO) was developed to detect LVO signs on NCCT.
  • The algorithm's performance was evaluated against CTA readings by experienced radiologists in a cohort of suspected acute stroke patients.
  • The model's accuracy was further tested with the addition of clinical variables (MethinksLVO+).

Main Results:

  • The MethinksLVO algorithm achieved an area under the curve (AUC) of 0.87 for LVO detection on NCCT.
  • The addition of clinical data (MethinksLVO+) improved the AUC to 0.91, with enhanced sensitivity and specificity.
  • Specific performance metrics included high sensitivity (83%) and improved specificity (85%) with MethinksLVO+.

Conclusions:

  • The MethinksLVO software demonstrates rapid and reliable prediction of LVO in acute stroke patients using NCCT.
  • This AI tool has the potential to reduce reliance on CTA, facilitate faster stroke network transfers, and enable quicker treatment decisions.