Liang Jiabin, Feng Jie, Lin Zhijie, Wei Jinbo, Luo Xun, Wang Qing Mei, He Bingjie, Chen Hanwei, Ye Yufeng
Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China.
Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Neurol. 2023 Dec 19;14:1294723. doi: 10.3389/fneur.2023.1294723. eCollection 2023.
Accurately assessing the prognostic outcomes of patients with acute ischemic stroke and adjusting treatment plans in a timely manner for those with poor prognosis is crucial for intervening in modifiable risk factors. However, there is still controversy regarding the correlation between imaging-based predictions of complications in acute ischemic stroke. To address this, we developed a cross-modal attention module for integrating multidimensional data, including clinical information, imaging features, treatment plans, prognosis, and complications, to achieve complementary advantages. The fused features preserve magnetic resonance imaging (MRI) characteristics while supplementing clinical relevant information, providing a more comprehensive and informative basis for clinical diagnosis and treatment. The proposed framework based on multidimensional data for activity of daily living (ADL) scoring in patients with acute ischemic stroke demonstrates higher accuracy compared to other state-of-the-art network models, and ablation experiments confirm the effectiveness of each module in the framework.
准确评估急性缺血性中风患者的预后结果，并及时为预后不良的患者调整治疗方案，对于干预可改变的风险因素至关重要。然而，关于急性缺血性中风基于影像学的并发症预测之间的相关性仍存在争议。为了解决这个问题，我们开发了一个跨模态注意力模块，用于整合多维数据，包括临床信息、影像特征、治疗方案、预后和并发症，以实现互补优势。融合后的特征保留了磁共振成像（MRI）特征，同时补充了临床相关信息，为临床诊断和治疗提供了更全面、更有价值的依据。与其他先进的网络模型相比，所提出的基于多维数据的急性缺血性中风患者日常生活活动（ADL）评分框架具有更高的准确性，消融实验证实了框架中每个模块的有效性。
