Developing next-generation, AI-enabled, medical image processing for multiple sclerosis clinical trials and routine care.

开发用于多发性硬化症临床试验和日常护理的下一代人工智能医学图像处理。

• 批准号: 2877679
• 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-2877679
• 关键词: Developing; next; generation; AI; enabled

Project Background:Multiple sclerosis (MS) is a disabling disease that causes disability in young persons costing the UK more than £3 billion per year. Images taken from brain and spinal cord can tell us what the current status of a patient is and how they may evolve. Current image processing tools to measure changes on brain and spinal cord are time consuming, require expensive and multi-step pipelines, and rely on the availability of high quality data which are often missing in the real world clinical practice. The candidate will develop a set of image processing tools using the latest advances in computer vision field to segment brain MRI scans, spinal cord MRI, and detect changes in these structures over time using data from both clinical trials and hospitals from across the UK. This project will use unique clinical trial datasets at the Queen Square Multiple Sclerosis Centre, MRI data from more than 8 hospitals from across the UK, and be able to test their potential for their applications in clinical trials in the industry with our collaborator (IXICO). This is a unique opportunity in between the academia and industry to develop the next generation of AI algorithms to impact drug development and patient care.Research aims:- Develop deep-learning based image simulators to enable efficient and semi-supervised image processing for the brain and spinal cord MRI (year 1 and year2)- Develop ultra-efficient image segmentation models for detecting disease activity and disease progression in multiple sclerosis using longitudinal image processing (year 2 and year 3)- Validate the developed models in clinical trials and real world data from our partner hospital (UCLH Trust / National Hospital for Neurology and Neurosurgery) and collaborating hospitals from across the UKThe outcome of the project will be a unique model that will receive either brain or spinal cord NHS MRI and will output volumes of relevant structures.

项目背景：多发性硬化症（MS）是一种致残性疾病，导致年轻人残疾，每年花费英国超过30亿英镑。从大脑和脊髓拍摄的图像可以告诉我们患者的当前状态以及它们可能如何演变。当前用于测量脑和脊髓变化的图像处理工具是耗时的，需要昂贵的多步骤流水线，并且依赖于高质量数据的可用性，而这些数据在真实的世界临床实践中经常缺失。候选人将使用计算机视觉领域的最新进展开发一套图像处理工具，以分割大脑MRI扫描，脊髓MRI，并使用来自英国各地临床试验和医院的数据检测这些结构随时间的变化。该项目将使用皇后广场多发性硬化中心独特的临床试验数据集，来自英国8家以上医院的MRI数据，并能够与我们的合作者（IXICO）一起测试其在行业临床试验中的应用潜力。这是学术界和工业界之间的一个独特机会，可以开发下一代人工智能算法，以影响药物开发和患者护理。- 开发基于深度学习的图像模拟器，为大脑和脊髓MRI实现高效和半监督的图像处理（第1年和第2年）-开发超高效的图像分割模型，用于使用纵向图像处理检测多发性硬化症的疾病活动和疾病进展（第2年和第3年）-在临床试验和来自我们合作医院的真实的世界数据中验证开发的模型（UCLH信托/国家神经病学和神经外科医院）和来自英国各地的合作医院该项目的成果将是一个独特的模型，将接收大脑或脊髓NHS MRI，并将输出相关结构的体积。