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stservice disability claims. Thrombus composition has been shown to be a major determinant of recanalization success and occurrence of complications in mechanical thrombectomy. The most important parameters of thrombus behavior during interventional procedures are relative fractions of fibrin and red blood cells (RBCs). We hypothesized that quantitative information from admission non-contrast CT (NCCT) and CT angiography (CTA) can be used for machine learning based prediction of thrombus composition. The analysis included 112 patients w

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