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

风湿性关节炎（RA）和银屑病关节炎（PsA）是两种最常见的炎性关节炎形式，其引起自身免疫诱导的关节炎症，导致患者的结构损伤、疼痛和显著残疾。目前的临床诊断和疾病的监测依赖于手，手腕和脚的普通X光片进行损伤评估。在临床研究和试验中已经提出并采用了几种放射学评分系统来进行损伤量化。然而，它们的应用受到手动评分的复杂性、观察者之间的变异性以及无法描述疾病表现的详细变化的限制，使得难以量化治疗效果和疾病进展。随着人工智能（AI）技术的发展，一些自动化的放射学诊断、分级和评分框架已被提出，并显示出良好的性能。然而，现有评分系统的内在问题尚未得到解决，大多数模型使用的方法提供有限的可解释性或可解释性。此外，尚未提出针对PsA的基于AI的影像学评分方法。该项目旨在为RA和PsA开发新的自动放射学定量方案，通过采用可解释和可解释的深度学习（DL）技术，可以提供疾病的更精细细节。一系列基于卷积神经网络的传统机器学习和DL方法将被实验。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，提出一个更可解释的模型，用于损伤量化。为了提供可解释性，事后解释方法，如特征加权和可视化的学习表示或模型将被用作基线。自我解释的模型结构，如原型变分编码器，学习的原型，可能与疾病阶段的特征空间和他们的投影在输入空间也将被探索，以及开发的损害评估方法，然后可以部署在现有的手和脚的X射线数据集连接到电子健康记录从RA或PsA患者研究疾病轨迹。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应答的变化。我们还将使用回顾性临床试验的数据对所提出的方法的性能进行验证，以期产生新的发现。所提出的研究将为RA和PsA建立新的可解释的自动量化方案，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。该项目将是合作的性质，包括与牛津银屑病关节炎中心和皇家联合医院巴斯合作。该项目属于EPSRC医疗保健技术研究主题和医学成像和人工智能技术研究领域的福尔斯。它将为开发临床相关的RA和PsA评估工具奠定基础，以促进临床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.