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End to end stroke triage using cerebrovascular morphology and machine learning

  • Deshpande, Aditi1, 2
  • Elliott, Jordan1
  • Jiang, Bin3
  • Tahsili-Fahadan, Pouya4, 5
  • Kidwell, Chelsea6
  • Wintermark, Max7
  • Laksari, Kaveh1, 2, 8
  • 1 Department of Biomedical Engineering, University of Arizona, Tucson, AZ , (United States)
  • 2 Department of Mechanical Engineering, University of California, Riverside, Riverside, CA , (United States)
  • 3 Department of Radiology, Stanford University, Stanford, CA , (United States)
  • 4 Department of Medical Education, University of Virginia, Inova Campus, Falls Church, VA , (United States)
  • 5 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD , (United States)
  • 6 Department of Neurology, University of Arizona, Tucson, AZ , (United States)
  • 7 Department of Neuroradiology, MD Anderson Center, University of Texas, Houston, TX , (United States)
  • 8 Department of Aerospace and Mechanical Engineering, University of Arizona, Tucson, AZ , (United States)
Published Article
Frontiers in Neurology
Frontiers Media SA
Publication Date
Oct 24, 2023
DOI: 10.3389/fneur.2023.1217796
  • Neurology
  • Original Research


Background Rapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning approach for automatic stroke triage. Methods Employing a validated convolutional neural network (CNN) segmentation model for image processing, we extract each patient’s cerebrovasculature and its morphological features from baseline non-invasive angiography scans. These features are used to detect occlusion’s presence and the site automatically, and for the first time, to estimate collateral circulation without manual intervention. We then use the extracted cerebrovascular features along with commonly used clinical and imaging parameters to predict the 90 days functional outcome for each patient. Results The CNN model achieved a segmentation accuracy of 94% based on the Dice similarity coefficient (DSC). The automatic stroke detection algorithm had a sensitivity and specificity of 92% and 94%, respectively. The models for occlusion site detection and automatic collateral grading reached 96% and 87.2% accuracy, respectively. Incorporating the automatically extracted cerebrovascular features significantly improved the 90 days outcome prediction accuracy from 0.63 to 0.83. Conclusion The fast, automatic, and comprehensive model presented here can improve stroke diagnosis, aid collateral assessment, and enhance prognostication for treatment decisions, using cerebrovascular morphology.

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