Abstract TP88: Deep Learning-based Automatic Detection And Segmentation Of Hyperdense Artery Sign On Non-contrast Computed Tomography In Acute Ischemic Stroke

Abstract

Introduction: CT- or MR-angiography(CTA/MRA) is the golden-standard to evaluate the site of large vessel occlusion (LVO). However, there are cases in which occluded vessels can be recognized as a hyperdense artery sign (HAS) on non-contrast CT (NCCT). If the HAS is detectable from NCCT, it may lead to a significant reduction in the time to start treatment. In this study, we developed and validated the deep learning model to detect the HAS on NCCT. Methods: This study used three multicenter retrospective datasets; ischemic stroke patients with LVO (occlusions of the intracranial internal carotid artery (ICA) and middle cerebral artery M1/M2 segments) who underwent mechanical thrombectomy, ischemic stroke patients without LVO, non-stroke patients. For developing the deep neural network model, positive samples (182 ICA/M1 and 57 M2 occlusion patients) were selected from stroke patients with LVO wherein the HAS was visible on NCCT. The decision for HAS visibility was made by two Neuro-interventionalists who referred to paired MRA/CTA of the same patient. While doing so, they also manually labeled the HAS regions on NCCT. Negative samples (592 patients) were collected from stroke patients without LVO and non-stroke patients. We split the data into training, validation and test sets. After we trained and validated the model, its performance was evaluated on the test set. The performance was measured using the metrics of sensitivity and specificity. Results: The sensitivity of the model was 0.78 (49/63) for ICA/M1 and 0.77 (10/13) for M2 occlusion patients. The specificity of the model was 0.61 (33/54) for stroke patients without LVO and non-stroke patients. Conclusions: The performance of our deep learning-based method to automatically detect the HAS on NCCT is close to that of expert physicians reported in earlier studies, and our method may lead to further improvement of patient prognosis by shortening the time to start treatment.