Evaluation of novel markers of on-set of PD using machine learning and routine CT brain imaging

该项目解决了早期发现PD发病的优先领域，以便及时实施新的治疗方法。该项目的主要目标是开发和评估一种基于人工智能（AI）的工具，用于分析医院图书馆的常规计算机断层扫描（CT）脑部扫描，以在常规诊断之前检测PD发病。这将带来一种新的方法来发现有患帕金森病风险的人并进行早期诊断。帕金森病是一种常见的神经退行性疾病（在英国大约有13万人受到影响，每年增加2%）。目前，该病的诊断是基于检查和运动障碍。我们相信CT脑改变可以作为在运动症状出现之前检测到的放射学标记。项目工作将包括开发基于深度学习方法的人工智能管道，以分析CT扫描以检测变化并评估变化作为PD的标记。这是一个跨学科项目，汇集了临床神经科学、放射学、电子学、计算机科学和统计学方面的专家。

This project addresses the priority area of early detection of PD onset to allow timely administration of new treatments. The main goal in this project is to develop and evaluate an artificial intelligence (AI)-based tool to analyse routine computer tomography (CT) brain scans in hospital libraries to detect PD onset before routine diagnosis. This will lead to a new way to detect people at risk of developing PD and to earlier diagnosis. PD is a common neurodegenerative disease (around 130, 000 people are affected in the UK, increasing by 2% per year). Currently, it is diagnosed on the basis of examination and movement disorder. We believe CT brain changes can serve as radiological markers detectable before the onset of movement symptoms. Project work will include developing an AI pipeline, based on deep learning methods, to analyse CT scans to detect the changes and evaluating the changes as markers of PD. This is an inter-disciplinary project which brings together experts in clinical neuroscience, radiology, electronics, computer science and statistics.