TY - JOUR
T1 - End to end stroke triage using cerebrovascular morphology and machine learning
AU - Deshpande, Aditi
AU - Elliott, Jordan
AU - Jiang, Bin
AU - Tahsili-Fahadan, Pouya
AU - Kidwell, Chelsea
AU - Wintermark, Max
AU - Laksari, Kaveh
N1 - Publisher Copyright: Copyright © 2023 Deshpande, Elliott, Jiang, Tahsili-Fahadan, Kidwell, Wintermark and Laksari.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - CNN—convolutional neural network
KW - cerebrovascular disease
KW - collateral circulation
KW - machine learning
KW - segmentation (image processing)
KW - stroke
KW - stroke outcome
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U2 - 10.3389/fneur.2023.1217796
DO - 10.3389/fneur.2023.1217796
M3 - Article
SN - 1664-2295
VL - 14
JO - Frontiers in Neurology
JF - Frontiers in Neurology
M1 - 1217796
ER -