Acute ischemic stroke (AIS) remains a global health challenge, leading to long-term functional disabilities without timely intervention. Spatio-temporal (4D) Computed Tomography Perfusion (CTP) imaging is crucial for diagnosing and treating AIS due to its ability to rapidly assess the ischemic core and penumbra. Although traditionally used to assess acute tissue status in clinical settings, 4D CTP has also been explored in research for predicting stroke tissue outcomes. However, its potential for predicting functional outcomes, especially in combination with clinical metadata, remains unexplored. Thus, this work aims to develop and evaluate a novel multimodal deep learning model for predicting functional outcomes (specifically, 90-day modified Rankin Scale) in AIS patients by combining 4D CTP and clinical metadata. To achieve this, an intermediate fusion strategy with a cross-attention mechanism is introduced to enable a selective focus on the most relevant features and patterns from both modalities. Evaluated on a dataset comprising 70 AIS patients who underwent endovascular mechanical thrombectomy, the proposed model achieves an accuracy (ACC) of 0.77, outperforming conventional late fusion strategies (ACC = 0.73) and unimodal models based on either 4D CTP (ACC = 0.61) or clinical metadata (ACC = 0.71). The results demonstrate the superior capability of the proposed model to leverage complex inter-modal relationships, emphasizing the value of advanced multimodal fusion techniques for predicting functional stroke outcomes.

中文翻译：

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一种基于交叉注意力的深度学习方法，用于使用 4D CTP 成像和临床元数据预测功能性卒中结果


急性缺血性中风 （AIS） 仍然是一项全球性的健康挑战，如果不及时干预，会导致长期功能障碍。时空 （4D） 计算机断层扫描灌注 （CTP） 成像对于诊断和治疗 AIS 至关重要，因为它能够快速评估缺血核心和半影带。虽然传统上用于在临床环境中评估急性组织状态，但 4D CTP 也已在预测卒中组织结果的研究中被探索。然而，它预测功能结果的潜力，尤其是与临床元数据相结合的潜力，仍未得到探索。因此，这项工作旨在通过结合 4D CTP 和临床元数据来开发和评估一种新的多模态深度学习模型，用于预测 AIS 患者的功能结果（特别是 90 天改良的 Rankin 量表）。为了实现这一目标，引入了一种具有交叉注意力机制的中间融合策略，以实现对两种模态中最相关的特征和模式的选择性关注。在包含 70 名接受血管内机械血栓切除术的 AIS 患者的数据集上进行评估，所提出的模型达到了 0.77 的准确率 （ACC），优于传统的晚期融合策略 （ACC = 0.73） 和基于 4D CTP （ACC = 0.61） 或临床元数据 （ACC = 0.71） 的单峰模型。结果表明，所提出的模型在利用复杂的模态间关系方面的卓越能力，强调了先进的多模态融合技术在预测功能性卒中结果方面的价值。