Computer-Aided Diagnosis of Hyperacute Stroke with Thrombolysis Decision Support Using a Contralateral Comparative Method of CT Image Analysis

Abstract

New and improved techniques have been continuously introduced into CT and MR imaging modalities for the diagnosis and therapy planning of acute stroke. Nevertheless, non-contrast CT (NCCT) is almost always used by every institution as the front line diagnostic imaging modality due to its high affordability and availability. Consequently, the potential reward of extracting as much clinical information as possible from NCCT images can be very great. Intravenous tissue plasminogen activator (tPA) has become the gold standard for treating acute ischemic stroke because it is the only acute stroke intervention approved by the FDA. ASPECTS scoring based on NCCT images has been shown to be a reliable scoring method that helps physicians to make sound decisions regarding tPA administration. In order to further reduce inter-observer variation, we have developed the first end-to-end automatic ASPECTS scoring system using a novel method of contralateral comparison. Due to the self-adaptive nature of the method, our system is robust and has good generalizability. ROC analysis based on evaluation of 103 subjects who presented to the stroke center of Chang Gung Memorial Hospital with symptoms of acute stroke has shown that our system's dichromatic classification of patients into thrombolysis indicated or thrombolysis contraindicated groups has achieved a high accuracy rate with AUC equal to 90.2 %. The average processing time for a single case is 170 s. In conclusion, our system has the potential of enhancing quality of care and providing clinical support in the setting of a busy stroke or emergency center.

Computer-Aided Detection of Hyperacute Stroke Based on Relative Radiomic Patterns in Computed Tomography

“…Consequently, the use of NCCT is intrinsically a quantitative and standardized process for infarct brain tissues. The previous literature suggested that since inherent anatomical structures in the human brain are symmetric, ischemic tissues can be highlighted by comparing the left and right sides of a brain [18,19].…”

Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients

“…6 Automated ASPECTS using machine learning has been developed to assist interpretation of NCCT scans. [7][8][9][10][11] Although prior validation studies of commercial computer systems for ASPECTS 7,8,[12][13][14][15][16][17][18] in comparison to human experts exist, these automated methods have not been evaluated using large independent data cohorts that have a variety of image acquisition parameters and patient characteristics. Validation of automated ASPECTS on small data sets from a limited number of sites may result in biased estimations of accuracy in favor of the automated system.…”

Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning

“…[13][14][15][16][17] In recent years, evidence that automated ASPECTS scoring methods based on machine learning are comparable with expert reading of ASPECTS is accumulating. [18][19][20][21][22][23][24] In this study, we developed an automated ASPECTS scoring system based on machine learning and feature engineering and compared it with expert ASPECTS readings on acute DWI. We introduced multiple highorder computational textural features into our machine learning model and hypothesized that this automated method can determine ASPECTS scores accurately and reliably compared with expert ASPECTS readings on acute DWI.…”