Next move in movement disorders (NEMO):Developing a computer-aided classification tool for hyperkinetic movement disorders

Introduction: Our aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool. We see this as the next step towards rapid and accurate phenotype classification, the cornerstone of both the diagnostic and treatment process. Methods and analysis: The Next Move in Movement Disorders (NEMO) study is a cross-sectional study at Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen. It comprises patients with single and mixed phenotype movement disorders. Single phenotype groups will first include dystonia, myoclonus and tremor, and then chorea, tics, ataxia and spasticity. Mixed phenotypes are myoclonus-dystonia, dystonic tremor, myoclonus ataxia and jerky/tremulous functional movement disorders. Groups will contain 20 patients, or 40 healthy participants. The gold standard for inclusion consists of interobserver agreement on the phenotype among three independent clinical experts. Electromyography, accelerometry and three-dimensional video data will be recorded during performance of a set of movement tasks, chosen by a team of specialists to elicit movement disorders. These data will serve as input for the machine learning algorithm. Labels for supervised learning are provided by the expert-based classification, allowing the algorithm to learn to predict what the output label should be when given new input data. Methods using manually engineered features based on existing clinical knowledge will be used, as well as deep learning methods which can detect relevant and possibly new features. Finally, we will employ visual analytics to visualise how the classification algorithm arrives at its decision. Ethics and dissemination: Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases

Visual Analytics Applied to Image Analysis:From Segmentation to Classification

Image analysis is the field of study concerned with extracting information from images. This field is immensely important for commercial and scientific applications, from identifying people in photographs to recognizing diseases in medical images.The goal behind the work presented in this thesis is providing mechanisms that allow humans to assist machines in image analysis tasks that are difficult to fully automate: image segmentation, feature selection, and image classification.Image segmentation is the task of partitioning an image into objects of interest (e.g., identifying which pixels correspond to a person in an image). In this context, we propose a new technique that enables faster interactive segmentation and potentially richer feature extraction, which may lead to increased efficacy. Image classification is the task of assigning a class label to an image based on generalization from examples (e.g., given images of a person, recognizing other images of this person). The traditional solution involves first representing each image by features (measurable characteristics) related to colors, textures, and shapes. In this context, we propose a new interactive visualization system that aims to provide insights that lead to the development of effective feature sets for image classification.We also show how this system can be adapted to explore intermediary computational results of artificial neural networks, with the goal of enabling insights about how these networks operate, which again may lead to improvements along the image classification pipeline. This task also leads to the development of a new time-dependent visualization technique

Where is your field going? A Machine Learning approach to study the relative motion of the domains of Physics

We propose an original approach to describe the scientific progress in a quantitative way. Using innovative Machine Learning techniques we create a vector representation for the PACS codes and we use them to represent the relative movements of the various domains of Physics in a multi-dimensional space. This methodology unveils about 25 years of scientific trends, enables us to predict innovative couplings of fields, and illustrates how Nobel Prize papers and APS milestones drive the future convergence of previously unrelated fields