Abstract WP405: Automated Detection of Hemorrhagic Stroke From Non-Contrast Computed Tomography: A Machine Learning Approach

Abstract

Introduction: Automated neuroimaging analysis is being used increasingly in the acute ischemic stroke (AIS) evaluation. However, current algorithms do not factor in an assessment of intracranial hemorrhage (ICH) in the workflow. In this study we present a machine learning (ML) algorithm that uses brain symmetry information to detect ICH. Methods: We prospectively collected non-contrast CT (NCCT) images on patients that presented to the Emergency Department for AIS evaluation between 2017 and 2019. Patients were included if they underwent technically adequate NCCT imaging. Diagnoses of ICH, AIS and non-stroke were confirmed by experienced neuroradiologists as well as review of the clinical record. A ML algorithm which integrates symmetry features as well as standard features for the whole brain was trained on 80% of the sample and validated on the remaining images. Training was performed without any prior segmentation. Evaluation of the model performance was conducted using receiver-operator curve and area under the curve (AUC) analysis. Results are given as median [IQR] and [AUC 95% CI]. Results: Among the 568 patients that met inclusion criteria, median age was 65 [55-76], 47% were female and 34% were white. 128 (23%) patients were determined to have ICH and 440 as non-ICH (70% AIS and 30% non-stroke). Among ICH patients, 108 (84%) had a supratentorial ICH. When analyzing the regions of the CT images that most strongly contributed to the algorithm’s diagnostic decisions, they corresponded with the regions of ICH (Fig. 1A). On the external validation data set, the algorithm successfully detected ICH (Fig. 1B) with high accuracy (AUC 0.99 [0.97-1.00]). Conclusion: We have developed a symmetry-sensitive ML method that can with very high fidelity identify ICH in an automated fashion. Without prior training, the algorithm autonomously was able to learn ICH location. These results may help contribute to an automated imaging workflow for all stroke evaluations, not just AIS.