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

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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.