Fully automated system for the analysis of the efficacy of knee replacement surgery

肌肉骨骼(MSK)疾病影响约16%的成年人和超过30%的65岁以上年龄组。随着大多数MSK疾病的发病率随着年龄的增长而增加，MSK疾病在英国等人口老龄化国家的社会经济影响正在稳步增加。2014-2016年间，英格兰、威尔士和北爱尔兰共进行了超过26万例初级膝关节置换手术。从短期或长期来看，KRS可能会导致并发症，需要翻修KRS，并可能导致危及生命的情况(例如，由松动的植入物引起)。目前，KRS失败的风险是术后10年的11%。当KRS失败时，重要的是安排一个修订的KRS，在膝关节受到不可逆转的伤害之前移除松动或损坏的植入物。英国所有NHS医院都在收集越来越多的医学图像，越来越多的人需要和机会利用这些信息来改善国家的健康。目前，医学图像在MSK疾病的临床实践和研究中尚未得到充分利用，其中2D X光图像因其可获得范围广、获取速度快和成本低而成为首选的成像技术。本项目的目的是开发一个自动化软件系统来研究KRS的有效性。该系统将在X光片中自动定位膝关节，并分析膝骨片或植入物的X光片形状和外观。然后，它将把获得的基于图像的数据与临床数据结合起来，旨在预测和识别早期关节衰竭的迹象。其目标是将临床收集的图像数据转化为有用的医疗信息，以造福于个人和社会层面的医疗保健。将进行各种临床安置，以确定临床需求并分析KRS决策中的临床工作流程。这涉及找到诸如如何修改当前工作流程以适应新的软件系统等问题的答案。与互联健康城市项目密切合作，将提供如何在现有数字医疗基础设施中技术上实施这样一个系统的细节。收集的信息将被用来为系统集成到临床环境中提供适当的设计(例如，图形用户界面)和实施策略。将在开发周期的不同阶段对软件系统进行验证，以确保其符合目的。该系统的可用性和可接受性将在项目接近尾声的试点试验期间进行评估。这项研究将产生一个计算机辅助系统，为KRS的决策过程和由此产生的医疗管理提供信息，提高临床实践中的护理质量。从长远来看，该项目旨在通过更好地了解KRS早期关节衰竭的情况，并通过在临床实践中提供识别和监测后者的计算机辅助系统，来改善普通人群的健康。这项工作将对以下方面产生积极影响：(I)患者，他们将受益于更好的治疗选择和管理，从而提高护理质量；(Ii)临床医生，他们将获得更多数据，做出明智的决定，并将通过使用自动化系统生成此类数据来节省时间；(Iii)医疗保健系统，该系统将受益于成本降低；(Iv)更广泛的研究界，在那里，新开发的成像和数据建模方法将有助于在自动图像分析的其他领域以及大规模数据分析方面的进步。

Musculoskeletal (MSK) diseases affect about 16% of adults and more than 30% in the age group 65+. As the incidence of most MSK diseases increases with age, the socio-economic impact of MSK diseases is increasing steadily in countries with an ageing population such as the UK. During 2014-2016, more than 260,000 primary knee replacement surgery (KRS) procedures were performed in England, Wales and Northern Ireland. KRS may, in the short or long run, lead to complications that will require a revision KRS and may result in life-threatening conditions (e.g., caused by a loose implant). Currently, the risk of KRS failure is 11% ten years post-operatively. When a KRS fails it is important to schedule a revision KRS to remove a loose or damaged implant before irreversible harm is done to the knee joint.There are an increasing number of medical images being gathered in all UK NHS hospitals, with the growing need and opportunity to utilise this information to improve the health of the nation. Currently, medical images are underutilised in clinical practise and research into MSK diseases where 2D radiographs are the imaging technique of choice due to wide availability, speed of acquisition and low cost.The aim of this project is to develop an automated software system to study the effectiveness of KRS. The system will automatically locate the knee joint in radiographs and analyse the radiographic shape and appearance of the knee bones or implant. It will then combine the obtained image-based data with clinical data aiming to predict and identify signs of early joint failure. The goal is to transform clinically collected image data into useful medical information to benefit healthcare at individual and societal levels. Various clinical placements will be undertaken to identify clinical needs and analyse the clinical workflow in KRS decision-making. This involves finding answers to questions such as how can the current workflows be revised to fit a new software system. Working closely with the Connected Health Cities project will provide details on how to technically implement such a system into the existing digital healthcare infrastructure. The collected information will be used to inform a suitable design (e.g., graphical user interface) and implementation strategy for the system to be integrated in the clinical setting. The software system will be validated at various stages of the development cycle to ensure that it fits the purpose. The usability and acceptability of the system will be evaluated during a pilot trial towards the end of the project. This research will result in a computer-aided system to inform the KRS decision-making process and the resulting medical management, improving the quality of care in clinical practice.In the long-term, this project aims to improve the health of the general population through a greater understanding of early joint failure in KRS, and by providing a computer-aided system for identifying and monitoring the latter in clinical practice. This work will have a positive impact on: (i) patients, who will benefit from better treatment choice and management, leading to improved quality of care; (ii) clinicians, who will have access to additional data to make an informed decision and who will save time by using an automated system to generate such data; (iii) the healthcare system, which will benefit from cost reductions; and (iv) the wider research community, where newly developed imaging and data modelling methods will contribute to advancements in other areas of automated image analysis as well as large scale data analysis.