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

arXiv · 2311.04772

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

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Abstract

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.

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BibTeX RIS

Xuhao Shan, Xinyang Li, Ruiquan Ge, Shibin Wu, Ahmed Elazab, Jichao Zhu, Lingyan Zhang, Gangyong Jia, Qingying Xiao, Xiang Wan, Changmiao Wang. 2023-11-08. GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration. https://arxiv.org/abs/2311.04772

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