用深度学习融合多模态数据预测中风治疗效果，助力临床决策
Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives
- 融合脑影像、传感器等多源数据建模
- 利用最终梗死数据提升长期功能预后预测精度
- 适合医疗AI研究者与临床决策支持系统开发者
中风是全球主要健康问题，导致死亡和残疾。预测中风干预结果可辅助临床决策并改善患者照护。深度学习能分析大量多样化的医学数据，包括脑部影像、病历报告及脑电图（EEG）、心电图（ECG）、肌电图（EMG）等传感器信息。尽管医学影像分析存在数据标准化挑战，未来深度学习在中风预后预测中的发展方向在于融合多模态信息，包括最终梗死数据，以更准确预测长期功能结局。本文综述了深度学习在中风预后预测中的最新进展与应用，涵盖（i）使用数据与模型，（ii）预测任务与成功指标，（iii）当前挑战与局限，以及（iv）未来方向与潜在益处。本全面综述旨在为研究人员、临床医生及政策制定者提供该快速演进且前景广阔领域的最新认知。
原文摘要 · Abstract (English)
Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports and other sensor information, such as EEG, ECG, EMG and so on. Despite the common data standardisation challenge within medical image analysis domain, the future of deep learning in stroke outcome prediction lie in using multimodal information, including final infarct data, to achieve better prediction of long-term functional outcomes. This article provides a broad review of recent advances and applications of deep learning in the prediction of stroke outcomes, including (i) the data and models used, (ii) the prediction tasks and measures of success, (iii) the current challenges and limitations, and (iv) future directions and potential benefits. This comprehensive review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field.
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