GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using
  Self-Attention with Domain Knowledge Integration
Xuhao Shan Xinyang Li Ruiquan Ge Shibin Wu Ahmed Elazab Jichao Zhu Lingyan Zhang Gangyong Jia Qingying Xiao Xiang Wan Changmiao Wang
Intracerebral Hemorrhage (ICH) is a severe condition resulting from damaged
brain blood vessel ruptures, often leading to complications and fatalities.
Timely and accurate prognosis and management are essential due to its high
mortality rate. However, conventional methods heavily rely on subjective
clinician expertise, which can lead to inaccurate diagnoses and delays in
treatment. Artificial intelligence (AI) models have been explored to assist
clinicians, but many prior studies focused on model modification without
considering domain knowledge. This paper introduces a novel deep learning
algorithm, GCS-ICHNet, which integrates multimodal brain CT image data and the
Glasgow Coma Scale (GCS) score to improve ICH prognosis. The algorithm utilizes
a transformer-based fusion module for assessment. GCS-ICHNet demonstrates high
sensitivity 81.03% and specificity 91.59%, outperforming average clinicians and
other state-of-the-art methods.