AI based diagnosis and support system for cartilage lesion detection on knee MRIs and automated rehabilitation assessment with quantitative biomarkers

基于人工智能的诊断和支持系统，用于膝关节 MRI 软骨病变检测和定量生物标志物自动康复评估

• 批准号: 2565765
• 金额: --
• 依托单位: University of Lincoln
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2565765
• 关键词: AI; based; diagnosis; support; system

Identifying cartilage lesions in patients undergoing MRI of the knee joint has many important implications in routine clinical applications. The MRI is commonly used to assess knee joint, especially cartilage lesions including cartilage softening, fissuring, diffuse thinning due to cartilage degeneration and acute cartilage injury. However, the diagnostic performance depends on the experience of operators and reader, and the different level of expertise also lead to the inter-observer variability in the clinical applications. Moreover, it is very challenging to quantifying these image based biomarkers from MRI due to the variations of their appearances. Therefore, developing a computer based methods for automated detecting cartilage lesions, qualifying and assessing the biomarkers following e.g. knee surgery on MRI would increase the diagnostic accuracy (performance) that would be beneficial to the patients while reducing the inter-observer variability and errors caused by human interpretation. In addition, the designed smart sensors equipped on the patients could provide dimensional information that help assessing the effectiveness of the treatment.This research proposal is anchored on a recent established collaboration between the Lincoln County Hospital, Prof Lee who is the Director of Research at the ULHT, and the Laboratory of Vision Engineering, School of Computer Science, University of Lincoln. The research aims to develop a fully automated AI based system to detect cartilage lesions and quantify the biomarkers within the knee joint on MRIs. Since the Artificial Intelligence (AI) has shown promising performance in some applications in the industry and has a lot of potentials to apply on a wide variety of applications in medical image analysis. This study will focus on applying the AI techniques on this application to deliver a novel AI diagnosis and treatment (recovery) assessment system of which the efficiency, accuracy and robustness will be validated in the clinical trial.To achieve this goal, the study is consisted of four phases:1. Developing the deep learning based anatomic structure (cartilage, bone, muscles) segmentation algorithm. Accurate segmentation is a key step for computer-based surgical planning of interventions affecting the knee.2. 3D Surface reconstruction /mapping of the segmented knee structures3. Automatically lesion detection by assessing structural anomalies within the segmented tissues from MRIs for diagnosis and treatment planning.4. Quantifying the knee joint degeneration using quantitative image-based biomarkers and textual data collected from smart sensors to evaluate the efficiency of treatment.In these four phases, the accurate segmentation in the phase 1 is a critical task, as it is a prerequisite stage for all the other phases.The outputs of the phase 2 and 3 would be beneficial to the diagnosis and treatment planning, and could reduce the errors and increase efficiency of the clinical work flow, due to its fully automated nature. Moreover, in the phase 4 we expect to propose a novel biomarker to evaluate the treatment and the stages of recovery based on imaging and wearable smart sensor data.

在常规的临床应用中，识别接受膝关节MRI检查的患者的软骨病变具有许多重要意义。MRI通常用于评估膝关节，特别是软骨病变，包括软骨软化、断裂、软骨退变引起的弥漫性变薄和急性软骨损伤。然而，诊断性能依赖于操作者和读者的经验，而不同的专业水平也导致了临床应用中观察者之间的差异。此外，由于这些基于图像的生物标记物的外观变化，从MRI中对其进行量化是非常具有挑战性的。因此，开发一种基于计算机的方法来自动检测软骨损伤，在MRI上对膝关节手术后的生物标记物进行鉴定和评估，将提高诊断的准确性(性能)，这将有利于患者，同时减少观察者之间的可变性和人为解释造成的错误。此外，安装在患者身上的智能传感器可以提供空间信息，帮助评估治疗的有效性。这项研究提案建立在林肯县医院、ULHT研究主任李教授和林肯大学计算机科学学院视觉工程实验室最近建立的合作基础上。这项研究的目的是开发一个全自动的基于人工智能的系统来检测软骨损伤，并在核磁共振成像上量化膝关节内的生物标记物。由于人工智能(AI)在行业中的一些应用中表现出了良好的性能，并在医学图像分析中具有广泛的应用潜力。本研究将致力于将人工智能技术应用于这一领域，提出一种新型的人工智能诊疗(康复)评估系统，该系统的效率、准确性和稳健性将在临床试验中得到验证。准确的分割是以计算机为基础的手术计划的关键一步，以影响膝关节的干预。分段膝关节结构的三维表面重建/映射3。通过评估来自磁共振成像的分割组织内的结构异常来自动检测病变，以用于诊断和治疗计划。使用基于图像的定量生物标志物和从智能传感器收集的文本数据来量化膝关节退变，以评估治疗的效率。在这四个阶段中，第一阶段的准确分割是关键任务，因为它是所有其他阶段的先决条件。第二阶段和第三阶段的输出将有利于诊断和治疗计划，并可以减少错误和提高临床工作流程的效率，因为其完全自动化的性质。此外，在第四阶段，我们希望提出一种新的生物标记物来评估治疗和康复阶段，基于成像和可穿戴智能传感器数据。