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Deep Learning-Based ASPECTS Scoring Method for Acute Ischemic Stroke

Physicians consider computed tomography (CT) as a gold standard to quickly diagnose the type of stroke and perform the Alberta stroke program early CT score (ASPECTS) to direct the use of tissue plasminogen activator (TPA) for the thrombolysis. Due to CT is less sensitive to ischemic changes in early Acute Ischemic Stroke (AIS), there is interobserver variability in the ASPECTS scores of physicians. The aim of this study is to develop a computer-assisted ASPECTS scoring system based on CT angiography (CTA) to reduce the difference between observers in clinical scoring, assist doctors in clinical decision-making, and improve the efficiency of stroke assessment. The high accuracy-rate test results on 20 subjects had demonstrated that ourmethod has the potential of providing clinical support.