Clinical outcome modelling of rapid dynamics in acute stroke with joint-detail, continuous, remote, body motion analysis

通过关节细节、连续、远程、身体运动分析对急性中风快速动力学进行临床结果建模

• 批准号: MR/T005351/1
• 负责人: Yee Mah
• 金额: $ 28.01万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT005351%2F1
• 关键词: Clinical; outcome; modelling; rapid; dynamics

Stroke - still the second commonest cause of death and principal cause of adult neurological disability in the Western World - is characterised by rapid changes over time and marked variability in outcomes. A patient may improve or deteriorate over minutes, and the resultant disability may range from an obvious complete paralysis to subtle, task-dependent incoordination of a single limb. Unlike many other neurological disorders, stroke can be exquisitely sensitive to prompt and intelligently tailored treatment, rewarding innovation in the delivery of care with real-world, tangible impact on patient outcomes. Optimal treatment therefore requires both detailed characterisation of the patient's clinical picture and its pattern of change over time. Arguably the most important aspect of the patient's clinical picture - body movement - remains remarkably poorly documented: quantified only subjectively and at infrequent intervals in the patient's clinical evolution. The combination of artificial intelligence with high-performance computing now enables automatic extraction of a patient's skeletal frame resolved down to major joints, like that of a stick-man, to be delivered simply, safely, and inexpensively, without the use of cumbersome body worn markers. Central to this technology is patient privacy, with the skeletal frame extracted in real time, ensuring no video data, from which patients can be identified, to be stored or transmitted by the device.Here we propose to use MoCat, our prototype motion categorisation system, to study the rapid dynamics of acute stroke, seamlessly embedded in the clinical stream. It can robustly determine the skeletal frame despite variations in patient size, clothing or presence of bed covering, and continuously monitor body motion at a short distance from the patient without need for extraneous wires or cables. Consequently, each bed on the hyperacute stroke unit will have its own dedicated device installed that will not interfere with the day-to-day activities on the ward. By quantifying the change in motor deficit over time we shall examine the relationship between these trajectories with clinical outcomes and develop predictive models that can support clinical management and optimise service delivery.Past work has shown the pattern of injury to the brain from a stroke impacts on the future outcome of the patient. We will therefore create models that combine our new body motion measures, with brain scans obtained routinely as part of the hyper acute stroke pathway. In this way we not only aim to improve the accuracy of our predictions, but also examine the relationship between the pattern of stroke brain injury and motor recovery.This project is aligned with a large-scale, Wellcome-funded, collaborative programme of translational research with the aim of creating a foundational framework for complex modelling of clinical and imaging data to predict outcomes in acute stroke.

中风-仍然是西方世界第二常见的死亡原因和成人神经系统残疾的主要原因-其特征是随着时间的推移发生快速变化，结果具有显著的变异性。患者可能在几分钟内改善或恶化，由此产生的残疾可能从明显的完全瘫痪到微妙的单肢任务依赖性不协调。与许多其他神经系统疾病不同，中风可以对及时和智能定制的治疗非常敏感，奖励在提供护理方面的创新，对患者的结果产生现实的、切实的影响。因此，最佳治疗需要详细描述患者的临床表现及其随时间变化的模式。可以说，患者临床表现的最重要方面-身体运动-仍然非常缺乏记录：仅主观量化，并且在患者的临床演变中间隔很短。人工智能与高性能计算的结合现在能够自动提取患者的骨骼框架，分解到主要关节，就像火柴人一样，简单，安全，廉价地交付，而无需使用笨重的身体穿戴标记。这项技术的核心是患者隐私，骨骼帧是真实的时间提取的，确保没有视频数据，从中可以识别出患者，被存储或传输的设备。在这里，我们建议使用MoCat，我们的原型运动分类系统，研究急性中风的快速动态，无缝地嵌入在临床流程中。它可以鲁棒地确定骨骼框架，而不管患者尺寸、衣服或床覆盖物的存在如何变化，并且在距离患者较短的距离处连续监测身体运动，而不需要额外的电线或电缆。因此，超急性卒中单元的每张床都将安装自己的专用设备，不会干扰病房的日常活动。通过量化运动缺陷随时间的变化，我们将研究这些轨迹与临床结局之间的关系，并开发预测模型，以支持临床管理和优化服务交付。过去的工作表明，中风对大脑的损伤模式会影响患者的未来结局。因此，我们将创建联合收割机，将我们新的身体运动测量与作为超急性卒中途径的一部分常规获得的脑部扫描相结合。通过这种方式，我们的目标不仅是提高预测的准确性，还包括检查中风脑损伤模式与运动恢复之间的关系。该项目与Wellcome资助的大规模转化研究合作计划相一致，旨在为临床和成像数据的复杂建模创建基础框架，以预测急性中风的结果。

项目成果

Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part VIII

医学图像计算和计算机辅助干预 - MICCAI 2022 - 第 25 届国际会议，新加坡，2022 年 9 月 18-22 日，会议记录，第八部分

• DOI: 10.1007/978-3-031-16452-1_67
• 发表时间: 2022
• 作者: Pinaya W

Machine learning-enabled multitrust audit of stroke comorbidities using natural language processing.

使用自然语言处理对中风合并症进行机器学习支持的多信任审计。

• DOI: 10.1111/ene.15071
• 发表时间: 2021
• 期刊: European journal of neurology
• 影响因子: 5.1
• 作者: Shek A