Data-Driven Methods for Microwave Sensor Devices in Musculoskeletal Diagnostics

Abstract: Microwave sensors can be used within medicine as they use non-ionizing radiation, are often low cost, and can be designed for a specific purpose. The application of microwave sensors for diagnostics and monitoring can be improved using appropriate data analysis. The multi-layered structure of the human body makes the measurements on people complex. A tremendous effort is required to create an analytical model of the body. In this context a data-driven approach, building a model that learns from previous measurements, is more suitable to analyze the data. This thesis aims to address statistical and data-driven approaches based on microwave sensor data for biomedical applications.A significant part of this thesis deals with microwave sensors for assessing muscle quality. It details the progress from initial clinical campaign to the creation of a machine learning algorithm to assess the local body composition. Such a device would be suitable for screening age-related muscle disorders like sarcopenia and muscle atrophy. Statistical analysis following the initial clinical campaign revealed no significant differences in the microwave data. Therefore, new sensor designs were evaluated. The most promising sensor was used in a small clinical campaign where it was able to detect a change in muscle size for one patient with multiple measurements over time. Successive measurements followed on tissue emulating phantoms and volunteers. For data analysis a machine learning algorithm was designed to predict the skin, fat, and muscle properties. This changes the aim from assessing muscle quality to assessing local body composition. For phantom data the algorithm was accurate for skin and fat and for volunteer data for fat and muscle. Crucially, the algorithm also performed better with more data available, meaning that results should improve if more data is collected.Microwave sensors have also been employed to assess bone. The first of two applications was to monitor the bone healing progression post surgery treating craniosynostosis. No substantial conclusions could be drawn from the statistical analysis most likely due to measurement uncertainties. The second application used a purpose-built setup for controlled measurements in ex vivo bone samples submerged in liquid, to simulate an in vivo environment. The purpose was to estimate the dielectric properties of bone. The derived bone properties were lower than expected, probably due to air trapped inside the sample.