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

识别接受膝关节MRI检查的患者的软骨病变在常规临床应用中具有许多重要意义。MRI常用于评估膝关节，特别是软骨病变，包括软骨软化、裂隙、由于软骨退变和急性软骨损伤引起的弥漫性变薄。然而，诊断性能取决于操作者和阅片者的经验，并且不同的专业水平也导致临床应用中观察者之间的差异。此外，由于它们的外观的变化，从MRI量化这些基于图像的生物标志物是非常具有挑战性的。因此，开发用于自动检测软骨病变、在例如膝关节手术后在MRI上鉴定和评估生物标志物的基于计算机的方法将提高诊断准确性（性能），这将有益于患者，同时减少观察者间变异性和由人类解释引起的误差。此外，患者身上配备的智能传感器可以提供尺寸信息，帮助评估治疗的有效性。这项研究计划是基于林肯县医院、ULHT研究总监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关系。该研究旨在开发一种基于人工智能的全自动系统，以检测软骨病变并量化MRI上膝关节内的生物标志物。由于人工智能（AI）在工业中的一些应用中表现出了良好的性能，并且在医学图像分析的各种应用中具有很大的潜力。本研究旨在将人工智能技术应用于此，以提供一种新的人工智能诊断和治疗（恢复）评估系统，并在临床试验中验证其效率、准确性和鲁棒性。开发基于深度学习的解剖结构（软骨、骨骼、肌肉）分割算法。准确的分割是基于计算机的手术规划的影响膝盖的干预措施的关键步骤。分割的膝关节结构的3D表面重建/映射3.通过评估MRI分割组织内的结构异常来自动检测病变，以进行诊断和治疗计划。利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对膝关节退行性变进行量化，以评估治疗效果。在这四个阶段中，第一阶段的准确分割是一项关键任务，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，并且由于其完全自动化的性质，可以减少错误并提高临床工作流程的效率。此外，在第四阶段，我们希望提出一种新的生物标志物，以评估基于成像和可穿戴智能传感器数据的治疗和恢复阶段。

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.