Keywords: Acute Ischemic Stroke, Computed Tomograph, Deep Learning, vision transformer
TL;DR: We propose an end-to-end deep learning model with transformer attention modules to predict final recanalization for acute ischemic stroke patients
Abstract: For acute ischemic stroke (AIS) patients with large vessel occlusions, clinicians must decide if the benefit of mechanical thrombectomy (MTB) outweighs the risks and potential complications following an invasive procedure. Pre-treatment computed tomography (CT) and angiography (CTA) are widely used to characterize occlusions in the brain vasculature. If a patient is deemed eligible, a modified treatment in cerebral ischemia (mTICI) score will be used to grade how well blood flow is reestablished throughout and following the MTB procedure. An estimation of the likelihood of successful recanalization can support treatment decision-making. In this study, we proposed a fully automated prediction of a patient’s recanalization score using pre-treatment CT and CTA imaging. We designed a spatial cross attention network (SCANet) that utilizes vision transformers to localize to pertinent slices and brain regions. Our top model achieved an average cross-validated ROC-AUC of 77.33 ± 3.9%. This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Application: Radiology
Secondary Subject Area: Detection and Diagnosis
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