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.

翻译：脑出血是一种因脑血管破裂导致的严重疾病，常引发并发症和死亡。由于其高死亡率，及时准确的预后评估和管理至关重要。然而，传统方法严重依赖临床医生的主观经验，可能导致诊断不准确和治疗延误。已有研究探索人工智能模型辅助临床诊断，但许多前期研究仅关注模型改进，未考虑领域知识。本文提出一种新型深度学习算法GCS-ICHNet，通过整合多模态脑部CT影像数据与格拉斯哥昏迷评分（GCS）改善脑出血预后评估。该算法采用基于Transformer的融合模块进行评估，具有81.03%的高灵敏度和91.59%的特异性，优于临床医生平均水平及其他最先进方法。