Health Data Science CDT

脊柱疾病是造成数百万人疼痛和痛苦的一个重要原因，也是一个重大的公共卫生负担，卫生服务费用非常高。脊柱侧弯引起椎体弯曲和旋转，产生脊柱侧弯。这可能会对患者的健康产生严重影响，包括：终生背痛、姿势问题、精神健康问题，严重的情况下还会导致心脏问题和呼吸问题。青少年特发性脊柱侧凸（AIS；儿童和青少年脊柱侧凸，原因不明）在英国和全球的患病率约为2-3%。AIS通常在青少年生长期间进展迅速，因此监测脊柱侧凸的进展至关重要。监控需要大量的资源。病例的自动监测将减少临床医生的工作量，使他们能够专注于复杂的病例，提高患者护理的质量和成本效益。自动分类也可用于人群水平筛查以确定病例。对脊柱侧凸的流行病学研究也很有限，部分原因是该病症的轻中度病例通常未被纳入行政卫生数据集。强大的医学图像自动分类将使进一步定量流行病学研究疾病的原因。我们有独特的医学影像数据来自英国生物银行和跨代雅芳纵向研究的父母和儿童（ALSPAC）。英国生物银行有大约48000个全身双能x射线吸收仪（DXA）扫描。ALSPAC对9000名儿童进行了大约33000次全身DXA扫描，这些儿童分别在9岁、13岁、15岁、17岁和24岁时进行了医学成像。允许我们使用时间序列的图像来预测青少年脊柱侧凸的进展。我们的目标是：1)开发人工智能工具，对医学图像中脊柱侧凸的存在进行分类，用于研究和临床使用；2)预测脊柱侧凸病例的进展。3)我们还旨在利用这些工具协助开展青少年特发性和退行性脊柱侧凸病因的流行病学研究。我们将开发机器学习（ML）和人工智能（AI）的新方法，以分类医学图像中脊柱侧凸的存在，并预测脊柱侧凸进展的可能性。为了实现这一目标，我们将使用机器学习和人工智能中计算机视觉和其他学科的最先进方法。我们将应用业界领先的神经网络架构来解决医学图像分类问题（以自主诊断脊柱侧凸），并将为此目的修改和开发新的架构和配置。此外，我们将开发新的时间序列分析方法，用于医学成像（预测脊柱侧凸进展的可能性）。总而言之：这个项目的新颖之处将包括应用和调整现有的行业领先的机器学习方法来进行医学图像分析，以及为此目的开发新的机器学习和人工智能方法。具体来说，这将集中在变压器架构和卷积神经网络的发展，用于医学图像的分析。该项目属于EPSRC信息和通信技术研究领域，其中人工智能技术是列出的主题之一。我们的重点是为医学领域的计算机视觉开发人工智能工具。该项目也属于生物信息学主题，因为我们正在开发用于医学和医学研究的人工智能技术，用于医疗数据的自动分析。该项目是与牛津大学和布里斯托尔大学肌肉骨骼研究部门的专家临床研究人员合作完成的，他们为我们的项目提供了临床基础。

Spinal disorders present a significant cause of pain and distress for millions of people and a significant public health burden with a very high cost to health services. Scoliosis causes a curvature and rotation of the vertebrae, producing a lateral curvature of the spine. This can have severe health implications for patients including: life-long back pain, posture issues, mental health problems and in severe cases: heart problems and respiratory issues. Adolescent Idiopathic Scoliosis (AIS; Scoliosis in children and teenagers, without a known cause) has a prevalence of approximately 2-3% in the British population and globally. AIS often progresses rapidly during periods of adolescent growth, making monitoring Scoliosis progression essential. Monitoring is incredibly resource intensive. Automated monitoring of cases would decrease the workload of clinicians, allowing them to focus on complex cases, increasing the quality and cost- effectiveness of patient care. Automated classification could also be used for population level screening to identify cases. Epidemiological research into Scoliosis is also limited, partially because mild-moderate cases of the condition are often not captured in administrative health datasets. Robust automated classification of medical images would enable further quantitative epidemiological research into the causes of the disease. We have unique medical imaging data from the UK Biobank and the transgenerational Avon Longitudinal Study of Parents and Children (ALSPAC). The UK Biobank has approximately 48,000 full-body Dual Energy X-ray Absorptiometry (DXA) scans. ALSPAC has approximately: 33,000 full-body DXA scans of 9,000 children taken as they grow up with medical images at ages: 9, 13, 15, 17, 24. Allowing us to use a timeseries of images to predict Scoliosis progression in adolescents.Our aims are to: 1) develop artificial intelligence tools to: classify the presence of Scoliosis in medical images for research use and clinical use; 2) predict the progression of Scoliosis cases. 3) We also aim to use these tools to assist with and conduct epidemiological research into the causes of Adolescent Idiopathic and Degenerative Scoliosis.We will develop novel methods in machine learning (ML) and Artificial Intelligence (AI) to classify the presence of Scoliosis in medical images and predict the likelihood of Scoliosis progressing. To achieve this, we will use state of the art methods from computer vision and other disciplines within ML and AI. We will apply industry leading Neural Network architectures to the problem of medical image classification (to autonomously diagnose Scoliosis) and will also modify, and develop, novel architectures and configurations for this purpose. Further, we will develop novel methods in timeseries analysis for use in medical imaging (to predict the likelihood of Scoliosis progression). To summarize: the novel aspects of this project will include applying and adapting existing industry leading ML methods to medical image analysis as well as developing novel ML and AI methods for this purpose. Specifically, this will focus on the development of Transformer architectures and Convolutional Neural Networks for the analysis of medical images. This project falls within the EPSRC information and communications technologies research area where Artificial Intelligence Technologies is one of the themes listed. Our focus specifically aligns to developing AI tools for computer vision in medicine. The project also falls within the Biological Informatics theme as we are developing artificial intelligence technologies for use in medicine and medical research for the automated analysis of medical data.The project is completed in collaboration with specialist clinical researchers at Oxford University and The University of Bristol Musculoskeletal Research Unit, who provide a clinical foundation for our project.