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Intracerebral Hemorrhage (ICH) is a severe condition resulting from damagedbrain blood vessel ruptures, often leading to complications and fatalities.Timely and accurate prognosis and management are essential due to its highmortality rate. However, conventional methods heavily rely on subjectiveclinician expertise, which can lead to inaccurate diagnoses and delays intreatment. Artificial intelligence (AI) models have been explored to assistclinicians, but many prior studies focused on model modification withoutconsidering domain knowledge. This paper introduces a novel deep learningalgorithm, GCS-ICHNet, which integrates multimodal brain CT image data and theGlasgow Coma Scale (GCS) score to improve ICH prognosis. The algorithm utilizesa transformer-based fusion module for assessment. GCS-ICHNet demonstrates highsensitivity 81.03% and specificity 91.59%, outperforming average clinicians andother state-of-the-art methods.

GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration