Towards a better understanding of cardio and cerebrovascular diseases

加深对心脑血管疾病的认识

• 批准号: 2872635
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2872635
• 关键词: Towards; better; understanding; cardio; cerebrovascular

Research Potential Impact:To identify biological and social factors that affect lifelong health, ageing and the development of chronic disease, and to use findings to design and test interventions that enhance healthy ageingResearch Aims:1. Discover novel features in medical imaging (e.g. echocardiography, magnetic resonance) to predict major clinical outcomes (e.g. heart attack, hospital admission, death).2. Use novel remote monitoring techniques (e.g. wearable devices and mobile Health devices) to identify trends significantly impacting on long-term cardiovascular health.3. Develop new predictive models of cerebrovascular disease evolution based on brain MRI imaging and clinical data.4. Improve the understanding of the possible links between cerebrovascular lesions and cognition.Research Methodology:Project will involve applying data science and machine learning techniques to physiological and imaging data to gain better insight into normal ageing and the disease process, with the overarching aim of improving prevention of cardio- and Cerebro-vascular disease and cognitive decline and promoting healthy ageing.Studentship is available in a partnership between the MRC Unit for Lifelong Health and Ageing, and the Centre for Medical Image Computing. Funding jointly from the MRC LHA, i4health CDT and the BHF accelerator award

研究潜在影响：确定影响终身健康、衰老和慢性病发展的生物和社会因素，并利用研究结果设计和测试促进健康老龄化的干预措施研究目的：1。发现医学成像（例如超声心动图、磁共振）的新特征来预测主要临床结果（例如心脏病发作、入院、死亡）。2.使用新颖的远程监控技术（例如可穿戴设备和移动健康设备）来识别对长期心血管健康有重大影响的趋势。3。基于脑MRI成像和临床数据开发脑血管疾病演变的新预测模型。4．提高对脑血管病变与认知之间可能联系的理解。研究方法：项目将涉及将数据科学和机器学习技术应用于生理和影像数据，以更好地了解正常衰老和疾病过程，总体目标是改善预防心脑血管疾病和认知能力下降以及促进健康老龄化。MRC 终身健康和老龄化部门与医学图像计算中心合作提供学生奖学金。由 MRC LHA、i4health CDT 和 BHF 加速器奖联合资助