Machine Learning on 3D Ultrasound Images and Wearable IoT Data for Brace Treatment of Spinal Deformities

基于 3D 超声图像和可穿戴物联网数据的机器学习用于脊柱畸形的支架治疗

• 批准号: RGPIN-2020-04415
• 负责人: Lou, Edmond
• 金额: $ 2.4万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752181
• 关键词: Machine; Learning; 3D; Ultrasound; Images

The science supporting the effectiveness of bracing to control spinal deformity is not clear. The mechanical response of the spine to physical loadings cannot be examined in real-time. This research aims to develop, advance and integrate monitoring technologies to investigate and understand how the biomechanical loadings applied from a simulated brace affects the internal spinal alignment. Four engineering challenges will be investigated:1) creation of a low cost 3D portable wireless ultrasound device, 2) development of machine learning (ML) algorithms to automatically extract 3D parameters from ultrasound images, 3) development of a software platform to record loads, positions and directions of pressure applied to a body and correlate with the internal alignment changes via real-time 3D ultrasound, and 4) design and development of wearable Internet-of-Things (IoT) devices to monitor the brace usage and control the interface pressure between the brace and patient's body to understand how spinal alignment may be optimized on an individual basis. A low cost 2D portable wireless ultrasound (US) device integrated with an electromagnetic position and orientation tracking system will be enhanced to create a 3D US scanner. The accuracy and the speed of the reconstruction process are important for real-time applications. ML algorithms based on convolutional neural networks will be developed to automatically extract parameters from ultrasound images. 300 US images will be used for training, 100 cases for testing and 100 cases for validation. This approach can reduce human measurement errors and training to allow non-ultrasound experts to extract information. A software platform integrated with a wireless sensing network to investigate the internal spinal alignment changes with different loads in real-time will be developed. A wireless pressure control sensor network built inside a custom standing frame has been embedded into the pressure pads to track spatial and pressure information. The developed portable 3D US will be used to capture internal alignment changes while manipulating the orientation, location and measured pressure from value of pressure pads. This innovate tool helps orthotists to obtain real-time feedback during brace design. An IoT device consists of a microcomputer system, a pressure sensor, air bladders embedded under the pressure area, a pump and valves feedback system, a temperature sensor and orientation sensor will be designed and built to monitor brace wear time and control wear brace tightness during daily living. This device can be programmed for individual needs to optimize brace effectiveness. The results of this research will be a suite of tools which can image the internal spinal alignment without ionizing radiation in real-time. The ML algorithms can be applied to other medical imaging applications with different training sets. The wearable IoT devices can be utilized for many other health monitoring systems.

支持支撑控制脊柱畸形有效性的科学尚不清楚。无法实时检查脊柱对物理负载的机械响应。这项研究旨在开发、推进和整合监测技术，以调查和了解模拟支架施加的生物力学载荷如何影响内部脊柱排列。将研究四个工程挑战：1) 创建低成本 3D 便携式无线超声设备，2) 开发机器学习 (ML) 算法以自动从超声图像中提取 3D 参数，3) 开发记录负载的软件平台，施加到身体的压力的位置和方向，并通过实时 3D 超声波与内部对准变化相关联，以及 4) 设计和开发可穿戴物联网 (IoT) 设备，以监控支具的使用情况并控制界面压力大括号之间和患者的身体，以了解如何根据个人情况优化脊柱排列。 集成了电磁位置和方向跟踪系统的低成本 2D 便携式无线超声 (US) 设备将得到增强，以创建 3D US 扫描仪。重建过程的准确性和速度对于实时应用非常重要。将开发基于卷积神经网络的机器学习算法，以自动从超声图像中提取参数。 300 个美国图像将用于训练，100 个案例用于测试，100 个案例用于验证。这种方法可以减少人为测量错误和培训，以允许非超声专家提取信息。 将开发一个与无线传感网络集成的软件平台，以实时研究不同负载下的内部脊柱对齐变化。内置在定制站立框架内的无线压力控制传感器网络已嵌入压力垫中，以跟踪空间和压力信息。开发的便携式 3D US 将用于捕获内部对准变化，同时根据压力垫值操纵方向、位置和测量压力。这种创新工具可帮助矫形师在支架设计过程中获得实时反馈。 物联网设备由微计算机系统、压力传感器、嵌入压力区域下的气囊、泵和阀门反馈系统、温度传感器和方向传感器组成，旨在监测支架磨损时间并控制磨损期间支架的松紧度。日常生活。该设备可以根据个人需求进行编程，以优化支架的有效性。 这项研究的结果将是一套工具，可以在没有电离辐射的情况下实时对内部脊柱排列进行成像。机器学习算法可以应用于具有不同训练集的其他医学成像应用。可穿戴物联网设备可用于许多其他健康监测系统。