Acute ischaemic stroke, caused by an interruption in blood flow to brain tissue, is a leading cause of disability and mortality worldwide. The selection of patients for the most optimal ischaemic stroke treatment is a crucial step for a successful outcome, as the effect of treatment highly depends on the time to treatment. We propose a transformer-based multimodal network (TranSOP) for a classification approach that employs clinical metadata and imaging information, acquired on hospital admission, to predict the functional outcome of stroke treatment based on the modified Rankin Scale (mRS). This includes a fusion module to efficiently combine 3D non-contrast computed tomography (NCCT) features and clinical information. In comparative experiments using unimodal and multimodal data on the MRCLEAN dataset, we achieve a state-of-the-art AUC score of 0.85.

翻译：急性缺血性中风由脑组织血流中断引起，是全球范围内导致残疾和死亡的主要原因。为患者选择最合适的缺血性中风治疗方案是取得良好预后的关键步骤，因为治疗效果高度依赖于治疗时机。我们提出了一种基于Transformer的多模态网络（TranSOP），采用分类方法，利用入院时获取的临床元数据和影像信息，基于改良Rankin量表（mRS）预测中风治疗的功能性结果。该方法包含一个融合模块，用于高效结合三维非增强计算机断层扫描（NCCT）特征与临床信息。在MRCLEAN数据集上使用单模态和多模态数据进行的对比实验中，我们取得了0.85的AUC评分，达到当前最优水平。