Development of a software system to automatically detect and quantify foot collapse

开发自动检测和量化足部塌陷的软件系统

• 批准号: 2453254
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2453254
• 关键词: Development; software; system; automatically; detect

Charcot arthropathy is collapse of a joint secondary to peripheral neuropathy, most commonly secondary to diabetes or alcoholism. It is a debilitating condition which causes progressive destruction of the joint with associated pain and alteration in the mechanics of walking. The foot often deforms, increasing the risk of joint instability, soft tissue ulceration, and ultimately lower extremity amputation. Prompt diagnosis allows patients to have appropriate podiatry assessment, orthotics, conservative bracing or surgical fixation. Current diagnosis and analysis of severity relies on radiologically derived geometric measurements on a lateral foot radiograph. These measurements are time consuming to obtain and are susceptible to inter- and intra-observer variability. This project will develop and validate a software system to automatically detect and quantify foot collapse. Machine-learning methods will be applied to automatically and reliably identify feature points on foot radiographs. The automatically identified feature points will then be used to automatically calculate geometric measurements of relevance to detecting and measuring foot collapse (e.g. Meary's angle). The performance of the system in obtaining the latter will be compared to manual ground truth measurements. Furthermore, Statistical Shape Models will be used to study the bone shape of the foot and ankle in diseased cases, aiming to improve methods for early detection and assessment of progression. The methods and software to be developed has the potential to save on reporting time for radiologists and to increase the reproducibility of disease-related geometric measurements. Furthermore, earlier detection of disease is likely to improve patient outcome and reduce costs to the NHS.The student will join a well-established research group, and will gain extensive experience of working in an interdisciplinary team. They will gain vast knowledge of state-of-the-art machine vision algorithm development for clinical imaging problems. The student will be placed in the translational space, and will have the opportunity to learn more about the pathway of progressing research towards impact in clinical practice. Finally, they will work with our clinical partners from Salford Royal NHS Foundation Trust and gain in-depth knowledge of foot and ankle anatomy and pathology, particularly the clinical imaging, diagnosis and treatment of Charcot arthropathy.

夏科关节病是继发于周围神经病变的关节塌陷，最常见继发于糖尿病或酗酒。这是一种使人衰弱的疾病，会导致关节逐渐破坏，并伴有疼痛和行走机制的改变。足部经常变形，增加关节不稳定、软组织溃疡并最终导致下肢截肢的风险。及时诊断使患者能够进行适当的足病评估、矫形器、保守支具或手术固定。目前的诊断和严重程度分析依赖于足外侧X光片上的放射学几何测量。获得这些测量结果非常耗时，并且容易受到观察者之间和观察者内部差异的影响。该项目将开发并验证一个软件系统，以自动检测和量化足部塌陷。机器学习方法将用于自动、可靠地识别足部射线照片上的特征点。然后，自动识别的特征点将用于自动计算与检测和测量脚塌陷相关的几何测量值（例如，梅里角）。系统在获得后者方面的性能将与手动地面实况测量进行比较。此外，统计形状模型将用于研究患病病例中足部和踝部的骨骼形状，旨在改进早期检测和进展评估的方法。待开发的方法和软件有可能节省放射科医生的报告时间，并提高与疾病相关的几何测量的可重复性。此外，早期发现疾病可能会改善患者的治疗效果并降低 NHS 的成本。学生将加入一个完善的研究小组，并将获得在跨学科团队中工作的丰富经验。他们将获得针对临床成像问题的最先进机器视觉算法开发的丰富知识。学生将被安置在转化空间中，并将有机会更多地了解研究进展对临床实践产生影响的途径。最后，他们将与来自索尔福德皇家 NHS 基金会信托基金的临床合作伙伴合作，深入了解足部和踝部解剖学和病理学，特别是夏科关节病的临床影像、诊断和治疗。