Predicting Tissue and Functional Outcome in Acute Stroke

预测急性中风的组织和功能结果

• 批准号: 10568740
• 负责人: Gregory George Zaharchuk
• 金额: $ 62.82万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10568740
• 关键词: Acute; Algorithms; Artificial Intelligence; Attention; Caring; Cerebral Infarction; Cerebrovascular Disorders; Cessation of life; Clinical; Clinical Data; Clinical Trials; Collaborations; Computer software; Data; Decision Making; Disease Progression; Disparate; Etiology; Functional disorder; Goals; Hemorrhage; Image; Individual; Infarction; Intervention; Ischemia; Ischemic Stroke; Lead; Learning; Location; Magnetic Resonance Imaging; Maps; Medical; Medicine; Methods; Minor; Modeling; Outcome; Pathway interactions; Patient Selection; Patients; Performance; Perfusion; Population; Process; Prospective cohort; Protocols documentation; Reperfusion Therapy; Research Personnel; Risk; Safety; Site; Speed; Stroke; Techniques; Testing; Thrombectomy; Time; Tissues; Training; Triage; Work; X-Ray Computed Tomography; acute stroke; artificial intelligence method; clinical practice; cohort; convolutional neural network; cost; data streams; deep learning; deep learning model; deep neural network; endovascular thrombectomy; experience; follow-up; functional outcomes; functional status; imaging study; improved; learning strategy; neural network; neuroimaging; outcome prediction; patient population; perfusion imaging; predictive modeling; randomized, clinical trials; stroke patient; stroke trials; tool; transfer learning; treatment effect

Abstract Stroke is a disabling cerebrovascular disease that causes 5.5 million deaths each year globally. The disease progresses rapidly and irreversibly, leaving a narrow time window for intervention. Existing methods for patient selection for endo- vascular thrombectomy are suboptimal, based exclusively on simple linear threshold models applied to neuroimaging. Deep learning has shown great promise in recent years for many medical applications. We believe that it can be used to integrate imaging and non-imaging data in a seamless and data- driven way to improve stroke triage and clinical trials. The goal of this project is to develop deep convolutional neural network approaches to the initial MR and CT imaging, the most commonly performed stroke imaging protocol in acute ischemic stroke patients, and to combine this with non-imaging clinical information. We will train networks to predict the most likely final tissue and clinical outcomes under 2 extreme conditions (major reperfusion and minimal reperfusion) to estimate the treatment effect at the individual level. Next, we use the methods and learning from this first study to train deep learning models without using contrast perfusion imaging, which will improve safety, cost, and time-to-treatment. Finally, we will test the generalizability and explainability of these AI methods in external cohorts which differ in terms of population and scanner types, including testing on data from mobile CT scanners. Accomplishment of these aims will fundamentally shift the acute stroke paradigm beyond the relatively simplistic mismatch concept and replace it with a data-driven method that takes into account the immense amount of imaging and clinical data that can be brought to the stroke decision-making process. The methods developed will improve long-term outcomes and reduce of the cost of stroke care worldwide.

抽象的 中风是一种致残性脑血管疾病，每年导致 550 万人死亡 全球范围内的一年。该疾病进展迅速且不可逆转，留下狭窄的范围 干预的时间窗口。现有的内镜患者选择方法 血管血栓切除术不是最理想的，仅基于简单的线性 应用于神经影像学的阈值模型。深度学习表现出色 近年来在许多医疗应用中都有希望。我们相信它可以 用于以无缝和数据方式集成成像和非成像数据 改善中风分类和临床试验的驱动方式。 该项目的目标是开发深度卷积神经网络 最初 MR 和 CT 成像的方法，最常用的方法 急性缺血性中风患者的中风成像方案，并将其结合起来 具有非影像学临床信息。我们将训练网络来预测 2种极端条件下最有可能的最终组织和临床结果 （主要再灌注和最小再灌注）来估计治疗效果 个人水平。接下来，我们使用第一项研究中的方法和经验 在不使用对比灌注成像的情况下训练深度学习模型，这 将提高安全性、成本和治疗时间。最后，我们将测试 这些人工智能方法在外部队列中的普遍性和可解释性 其在人口和扫描仪类型方面有所不同，包括数据测试 来自移动 CT 扫描仪。 这些目标的实现将从根本上改变急性中风的现状 范式超出了相对简单的失配概念，并将其替换为 考虑到大量成像的数据驱动方法 以及可用于中风决策过程的临床数据。 所开发的方法将改善长期结果并减少 全球中风护理费用。