Journal article
Computed Tomography Perfusion-Based Machine Learning Model Better Predicts Follow-Up Infarction in Patients With Acute Ischemic Stroke
H Kuang, W Qiu, AM Boers, S Brown, K Muir, CBLM Majoie, DWJ Dippel, P White, J Epstein, PJ Mitchell, A Dávalos, S Bracard, B Campbell, JL Saver, TG Jovin, M Rubiera, AV Khaw, JJ Shankar, E Fainardi, MD Hill Show all
Stroke | Published : 2021
Abstract
Background and Purpose: Prediction of infarct extent among patients with acute ischemic stroke using computed tomography perfusion is defined by predefined discrete computed tomography perfusion thresholds. Our objective is to develop a threshold-free computed tomography perfusion-based machine learning (ML) model to predict follow-up infarct in patients with acute ischemic stroke. Methods: Sixty-eight patients from the PRoveIT study (Measuring Collaterals With Multi-Phase CT Angiography in Patients With Ischemic Stroke) were used to derive a ML model using random forest to predict follow-up infarction voxel by voxel, and 137 patients from the HERMES study (Highly Effective Reperfusion Evalu..
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Funding Acknowledgements
Dr Menon holds the Heart and Stroke/University of Calgary Professorship in Stroke Imaging and Alberta Innovate Health Solution funding.