Mobile Assisted Reconstruction In Orthopaedics: extended to the fully automated surgical navigation system

到2040年，全球11.4%的成年人将经历由关节炎引起的活动受限。这些限制导致了痛苦、社会孤立和孤独。在50岁以上的成年人中，继发于骨关节炎的膝关节疼痛导致的残疾发生率高达20%，80岁以上的成年人中，这一比例为40%。全膝关节置换术(TKA)是治疗膝关节骨性关节炎最具成本效益和持续成功的手术之一。患者报告的结果显示，在疼痛缓解、功能恢复和生活质量改善方面有显著改善。2019年，英国和美国分别进行了约10万次和100万次膝关节置换手术。由于人口和生活方式的变化，这一数字还在增加。然而，10%-20%的TKA患者并不完全满意，这在一定程度上是由于手术方式的变化。改进手术的先进技术是机器人技术和患者专用器械。这些都是昂贵的(机器人)，而且很难实施(针对患者的器械)。通过利用智能手机技术的发展，我们正在开发移动辅助重建整形外科(M.A.R.I.O)，这是一种简单、低成本的术中导航系统，可以帮助外科医生更准确地放置假体。术前规划是矫形手术成功的必要前提。我们开发了一种统计形状模型(SSM)技术，可以从X射线或CT扫描图像中预测完整的、非病理性的骨形状，并利用这一技术自动生成更好的手术计划。该计划在手术期间由基于智能手机的导航应用程序使用。医学图像分割是SSM可以进行的关键步骤，目前的黄金标准是手动绘制感兴趣区域。用户和植入物制造商的反馈鼓励我们现在开发一种自动图像分割工具，作为我们M.A.R.I.O设备的一部分。机器学习系统正在以更高的精确度、更少的人力和更快的速度慢慢取代由医学专家和放射科医生执行的人工任务。ICL目前最先进的基于机器学习的医学图像分割研究，ICL世界领先的医疗器械翻译基础设施，以及高速计算机的出现和它们低廉的价格，使我们处于一个理想的位置，可以自动分割骨骼和软骨形状，这仍然没有实现。当前项目的总成本相当于50个修订手术，我们将证明一个全自动手术前规划软件的可行性，从长远来看，它将使患者、外科医生和NHS受益。

By 2040, 11.4 % of all adults will experience arthritis-attributed activity limitations globally. These limitations lead to pain, social isolation and loneliness. The incidence of disability from knee pain secondary to osteoarthritis is high at 20% of adults over 50 and 40% over 80 years. Total knee arthroplasty (TKA) is one of the most cost-effective and consistently successful surgeries performed to treat osteoarthritis of the knee. Patient-reported outcomes are shown to improve dramatically with respect to pain relief, restoration of function, and improved quality of life. In 2019, around 100,000 and 1,000,000 knee replacements were performed in the UK and the US, respectively. This number is increasing due to demographic and lifestyle changes. However, 10-20% of TKA patients are not fully satisfied, and this is partly attributed to variations in surgery.The advanced technologies that improve the surgery are robotic technologies and patient-specific instrumentation. These are expensive (robots) and difficult to implement (patient-specific instrumentation). By taking advantage of the evolution of smartphone technology, we are developing Mobile Assisted Reconstruction In Orthopaedics (M.A.R.I.O), a simple, low-cost, intra-operative navigation system to help surgeons place implants more precisely.Pre-operative planning is an essential prerequisite for the success of the orthopaedic procedure. We have a developed Statistical Shape Model (SSM) technology that can predict the intact, nonpathological bone shape from X-rays or CT scan images, and we use this to generate a better surgical plan automatically. This plan is utilised by a smartphone-based navigation application during surgery.The segmentation of medical images is a key step before SSM can take place and the current gold standard is to manually draw the region-of-interest. Users' and implant manufacturers' feedback is encouraging us to develop an automated image segmentation tool now, as part of our M.A.R.I.O device. Machine-learning systems are slowly replacing the manual tasks being carried out by medical experts and radiologists with higher accuracy, reduced human effort, and greater speed. The current state-of-the-art research on machine learning-based medical image segmentation at ICL, ICL's world-leading medical device translation infrastructure and the advent of high-speed computers and their modest price is placing us in an ideal position to automate the segmentation of bone and cartilage shapes, which has remained unachieved.The overall cost of the current project is equivalent to 50 revision surgeries, we will prove the feasibility of a fully automated pre-surgical planning software to benefit patients, surgeons, and the NHS in the longer term.