Clinically trusted Artificial Intelligence and medical image analysis for monitoring inflammatory arthritis

类风湿性关节炎(RA)和银屑病关节炎(PSA)是两种最常见的炎症性关节炎，可引起自身免疫性关节炎症，导致结构损伤、疼痛和患者严重残疾。目前对这些疾病的临床诊断和监测依赖于手、腕和脚的普通X光片来进行损害评估。已经提出了几种放射学评分系统，并将其用于临床研究和试验中，以进行损伤量化。然而，由于人工评分的复杂性、观察者之间的可变性以及无法描述疾病表现的详细变化，它们的应用受到限制，因此很难量化治疗效果和疾病进展。作为一个耗时的过程，评分在临床诊断和疾病进展监测中很少进行。随着人工智能(AI)的进步，已经提出了几个用于RA的自动放射诊断、分级和评分框架，显示出良好的性能。然而，现有评分系统的内在问题尚未得到解决，而且大多数模型使用的方法提供的解释性或可解释性有限。此外，还没有建立基于人工智能的放射学评分方法，用于PSA。本项目旨在开发新的针对RA和PSA的自动放射成像量化方案，通过采用可解释和可解释的深度学习(DL)技术来提供更详细的疾病细节。将对一系列基于卷积神经网络的传统机器学习和DL方法进行实验。我们计划采用相似性排序的概念，直接比较图像中的解剖结构，以提出一个更具解释性的损伤量化模型。为了提供可解释性，将使用特征加权和学习的表示或模型的可视化等事后解释方法作为基线。还将探索自解释的模型结构，例如原型变分编码器，它学习可能与特征空间中的疾病阶段相关的原型及其在输入空间中的投影。然后，开发的损伤评估方法可以部署在现有的手部和脚部X射线数据集上，这些数据集链接到RA或PSA患者的电子健康记录，以研究疾病轨迹。将使用临床数据进行亚组分析，以确定疾病进展的亚型和治疗反应的变化。建议的研究将为RA和PSA建立新的可解释的自动化量化方案，这些方案可用于更深入地了解疾病在临床环境中的表现以及影响其进展的潜在治疗或个人特征。该项目在性质上将是合作的，包括与牛津银屑病关节炎中心和皇家联合医院巴斯的合作。该项目属于EPSRC医疗保健技术研究主题以及医学成像和人工智能技术研究领域。这将为开发与临床相关的RA和PSA评估工具奠定基础，以促进临床上的治疗决策和临床试验中的治疗效果评估。

Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are the two most prevalent forms of inflammatory arthritis that cause autoimmune-induced joint inflammation leading to structural damage, pain and significant disability in patients. Current clinical diagnosis and monitoring of the diseases rely on plain radiographs of hands, wrists, and feet for damage assessment. Several radiographic scoring systems have been proposed and adopted in clinical research and trials for damage quantification. However, their application is limited by the complexity of manual scoring, inter-observer variability and failure to describe detailed variations in disease manifestation, making it difficult to quantify treatment effects and disease progression. As a time-consuming process, scoring is rarely performed in clinical diagnosis and monitoring of the disease progression.With advances in Artificial Intelligence (AI), several automated radiographic diagnosis, grading and scoring frameworks have been proposed for RA, demonstrating promising performance. Nevertheless, the intrinsic issues with existing scoring systems have not been addressed, and most of the models use methods that provide limited interpretability or explainability. In addition, no established AI-based radiographic scoring approaches have been proposed for PsA. This project aims to develop novel automated radiographic quantification schemes for RA and PsA which could provide finer details of the diseases by adopting interpretable and explainable Deep Learning (DL) techniques. A range of conventional machine learning and DL methods based on convolutional neural networks will be experimented with. We plan to employ the concept of similarity ranking that directly compares the anatomical structure in images to propose a more interpretable model for damage quantification. To provide explainability, post-hoc explanation methods such as feature weighting and visualisation of learned representations or models will be utilised as the baseline. Self-explainable model structures such as prototype variational encoders which learn the prototypes that may be linked to disease stages in the feature space and their projections in the input space will be explored as well.The developed damage assessment method could then be deployed on existing hand and feet X-ray datasets linked to electronic health records from RA or PsA patients to study disease trajectories. Subgroup analysis will be performed using clinical data to identify subtypes of disease progression and variations in treatment response. The performance of the proposed methods will be also validated using data from the retrospective clinical trials in the hope of generating novel discoveries.The proposed studies will establish new explainable automated quantification schemes for RA and PsA that could be applied to grant greater insight into the manifestation of the diseases in clinical settings and potential treatment or personal features that affect their progression in time. The project will be collaborative in its nature including collaboration with Oxford Psoriatic Arthritis Centre and Royal United Hospitals Bath. The project falls within the EPSRC Healthcare Technologies research theme and the Medical Imaging and AI Technologies research areas. It will lay the foundation for the development of clinically relevant RA and PsA evaluation tools to facilitate treatment decisions in the clinic and treatment effect assessments in clinical trials.