Causal modelling of disease progression in medical images and associated clinical data

研究背景的简要描述，包括潜在的影响该项目旨在将疾病进展建模技术与深层结构因果模型相结合，以反事实地推断患者的特征，并合成代表这种因果模型的真实图像-例如，预测患者可能对给定的干预措施做出的反应，或者如果他们停止吸烟，他们的预后可能会如何变化。最终，这可以为临床推断提供信息，并被用来更好地教育医生和他们的患者有关疾病轨迹的知识。目的和目标--具体目标是：利用结构性因果模型来限制对阿尔茨海默病长期疾病轨迹的推断。模型将能够根据现有的假设从多个临床协变量预测疾病的进展，并合成与预测的进展合理匹配的真实图像(S)。将结构因果模型应用于不规则采样的数据集，和/或表示短期纵向自然历史，甚至是横截面的，以产生现实的预测。为多模式数据集建立有效的技术。合成图像和相关的医疗保健数据，这是足够现实的，以支持临床推断。揭示各种临床因素对患者轨迹的影响，以应用于神经或呼吸系统疾病。研究方法的新颖性深层结构因果模型(DSCM)使用归一化流程和变分推理来实现对噪声变量的易于推理。这代表着对现有带有深度学习组件的因果学习方法的发展。DSCM已经在合成和真实世界的数据集上得到了验证，它能够表示所有三个层次的因果关系；即关联、干预和反事实。使用这项技术和相关技术可以对复杂疾病进行现实的、因果的图像建模，最终可以更好地了解感兴趣的情况。与EPSRC的战略和研究领域保持一致该项目与EPSRC的医疗技术主题和以下研究领域保持一致：人工智能技术、临床技术、图像和视觉计算、医学成像统计和应用概率任何公司或合作者都参与了微软剑桥研究院

Brief description of the context of the research including potential impactThe project aims to combine disease progression modelling techniques with deep structural causal models to counterfactually reason about patient characteristics and synthesise realistic images which represent this causal modelling - for example, to predict how a patient might respond to a given intervention, or how their prognosis might change if they stop smoking. Ultimately, this could inform clinical inference, as well as be used to better educate doctors and their patients about disease trajectories. Aims and Objectives -The specific objectives are to: Exploit structural causal models to constrain inference of long-term disease trajectory in Alzheimer's disease. Models will be able to forecast disease progression from multiple clinical covariates according to existing hypotheses, and of synthesising realistic images that reasonably match predicted progression(s). Apply structural causal modelling in datasets which are irregularly sampled, and/or represent short-term longitudinal natural histories, or are even cross-sectional to produce realistic predictions. Establish effective techniques for multi-modal datasets. Synthesize both image and associated healthcare data which is realistic enough to support clinical inference. Uncover the effects of various clinical factors on patient trajectories for applications such as neurological or respiratory diseases. Novelty of Research Methodology The Deep Structural Causal Model (DSCM) uses normalising flows and variational inference to enable tractable inference of noise variables. This represents a development over existing causal learning methods with deep learning components. The DSCM has been validated on synthetic and real-world datasets for its ability to represent all three levels of causation; namely association, intervention, and counterfactuals. Use of this and associated techniques can allow for realistic, causal image modelling of complex disease, which can ultimately allow for a better understanding of the condition of interest. Alignment to EPSRC's strategies and research areas The project aligns with EPSRC's Healthcare technologies theme and the following research areas: Artificial intelligence technologies Clinical technologies Image and vision computing Medical imaging Statistics and applied probability Any companies or collaborators involved Microsoft Research Cambridge