GCS-ICHNet：基于融合领域知识的自注意力机制的脑出血预后评估
GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration
AI总结：
针对脑出血传统预后方法依赖主观经验、现有AI模型未结合领域知识的问题，提出融合CT影像与GCS评分、基于transformer融合模块的GCS-ICHNet算法，其灵敏度、特异度优于临床医生及现有最优方法。
摘要：
脑出血（ICH）是脑血管破裂受损导致的严重病症，常引发并发症与死亡。由于其死亡率高，及时准确的预后评估与处置至关重要。但传统方法高度依赖临床医生的主观经验，可能导致诊断不准确、治疗延误。已有研究探索人工智能（AI）模型辅助临床医生，但多数前期研究聚焦模型改进，未结合领域知识。本文提出新型深度学习算法GCS-ICHNet，融合多模态脑部CT影像数据与格拉斯哥昏迷量表（GCS）评分以提升ICH预后效果，该算法采用基于transformer的融合模块进行评估。GCS-ICHNet实现了81.03%的高灵敏度与91.59%的特异度，表现优于普通临床医生及其他现有最优方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
