AI-powered emotion recognition, typing with thoughts or eavesdropping virtual assistants: three non-fictional examples illustrate how AI may impact society. AI-related products and services... Show moreAI-powered emotion recognition, typing with thoughts or eavesdropping virtual assistants: three non-fictional examples illustrate how AI may impact society. AI-related products and services increasingly find their way into daily life. Are the EU's fundamental rights to privacy and data protection equipped to protect individuals effectively? In addressing this question, the dissertation concludes that no new legal framework is needed. Instead, adjustments are required. First, the extent of adjustments depends on the AI discipline. There is nothing like 'the AI'. AI covers various concepts, including the disciplines machine learning, natural language processing, computer vision, affective computing and automated reasoning. Second, the extent of adjustments depends on the type of legal problem: legal provisions are violated (type 1), cannot be enforced (type 2) or are not fit for purpose (type 3). Type 2 and 3 problems require either adjustments of current provisions or new judicial interpretations. Two instruments might be helpful for more effective legislation: rebuttable presumptions and reversal of proof. In some cases, the solution is technical, not legal. Research in AI should solve reasoning deficiencies in AI systems and their lack of common sense. Show less
Contrary to common belief, sign languages are distinct across different communities and cultures, evolving organically through interactions among deaf people, rather than being based on spoken... Show moreContrary to common belief, sign languages are distinct across different communities and cultures, evolving organically through interactions among deaf people, rather than being based on spoken languages. Each sign language has its own grammar, vocabulary, and cultural nuances, with variations even within a single country, showcasing the diverse communication methods within the deaf community. Deaf individuals often face encouragement to use spoken language techniques like lipreading or text communication, highlighting a bias towards spoken languages. This is compounded by the lack of sign languages in linguistic technologies, emphasizing the need for more inclusive research and development. This dissertation aims to address this gap using machine and deep learning to improve sign language processing and recognition. It covers six chapters, introducing methods for video-based sign annotation, webcam-based sign language dictionary search, and ranking systems for sign suggestions. It also explores tools for visualizing and comparing sign language variation, contributing valuable resources to linguistic research. Show less
This thesis investigates the contribution of quantum computers to machine learning, a field called Quantum Machine Learning. Quantum Machine Learning promises innovative perspectives and methods... Show moreThis thesis investigates the contribution of quantum computers to machine learning, a field called Quantum Machine Learning. Quantum Machine Learning promises innovative perspectives and methods for solving complex problems in machine learning, leveraging the unique capabilities of quantum computers. These computers differ fundamentally from classical computers by exploiting certain quantum mechanical phenomena. The thesis explores various proposals within quantum machine learning, such as the application of quantum algorithms in topological data analysis. With respect to topological data analysis, results demonstrate that quantum algorithms solve problems considered inefficient in classical settings. The thesis also explores structural risk minimization in quantum machine learning models, identifying crucial design choices for new quantum machine learning models. Additionally, it introduces quantum models in reinforcement learning, which deliver comparable performance to traditional models and are superior in certain scenarios. The final part identifies learning tasks in computational learning theory where quantum learning algorithms have exponential advantages. In summary, this thesis contributes to understanding how quantum computers can address complex machine learning problems, from topological data analysis to reinforcement learning and computational learning tasks. Show less
The research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency,... Show moreThe research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency, which is evaluated based on donors' hemoglobin and ferritin levels. If either of these levels are inadequate, donors are deferred from donation. Deferral due to low hemoglobin levels occurs on-site, meaning that donors have already traveled to the blood bank and then have to return home without donating, which is demotivating for the donor and inefficient for the blood bank. A large part of this dissertation therefore has the objective to develop a prediction model for donors' hemoglobin levels, based on historical measurements and donor characteristics.The prediction model that was developed reduces the deferral rate by approximately 60\% (from 3\% to 1\% for women, and from 1\% to 0.4\% for men), showing the potential of using data to enhance blood bank policy efficiency. Additionally, the model predictions were made explainable, providing the blood bank with insights into why specific predictions are made. These insights increase our understanding of the relationships between donor characteristics and hemoglobin levels. If this prediction model would be implemented in practice, the explanations could also be shared with the donor to help them understand why they are (not) invited to donate, which could also contribute to donor satisfaction and retention.In a collaborative effort with blood banks in Australia, Belgium, Finland and South Africa, the same prediction model was applied on data from each blood bank. Despite differences in blood bank policies and donor demographics, the models found similar associations with the predictor variables in all countries. Differences in performance could mostly be attributed to differences in deferral rates, with blood banks with higher deferral rates obtaining higher model accuracy.Beyond hemoglobin prediction models, additional research questions are explored. One study aims to identify determinants of ferritin levels in donors through repeated measurements, and linking these to environmental variables. Another study involves modeling the pharmacokinetics of antibodies in COVID-19 recovered donors, and finding relationships between patient characteristics, symptoms, and antibody levels over time.In summary, the research in this dissertation shows the potential within the wealth of data collected by blood banks. The proposed data-driven donation strategies not only decrease deferral rates but also increase donor retention and understanding. This comprehensive approach allows Sanquin to provide more personalised feedback to donors regarding their iron status, ultimately optimising the blood donation process and contributing to the overall efficacy of blood banking systems. Show less
Bacteriophages, or phages for short, are the most abundant biological entity in nature. They shape bacterial communities and are a major driving force in bacterial evolution. Their ubiquitous... Show moreBacteriophages, or phages for short, are the most abundant biological entity in nature. They shape bacterial communities and are a major driving force in bacterial evolution. Their ubiquitous nature and their potential use in medical and industrial applications make them attractive targets for fundamental and applied scientific studies. Understanding their structure and function at the molecular level is essential for understanding phage life cycles. In this thesis, I applied different cryo-EM techniques combined with advanced image processing and artificial intelligence methods to gain insight into structure and function of two bacteriophages. In both cases, these phages contain flexible elements which are essential for the infection process. While biologically highly interesting, these flexible components are especially challenging for structural studies. With the advances in computer technology and electron microscopy, researchers can now use various research methods to study different proteins and the structure and function of biological macromolecular machines. The studies presented in this thesis provide valuable insights into phages with flexible components, and provide a useful workflow for researchers with similar research topics. Show less
Transport inspectorates are looking for novel methods to identify dangerous behavior, ultimately to reduce risks associated to the movements of people and goods. We explore a data-driven approach... Show moreTransport inspectorates are looking for novel methods to identify dangerous behavior, ultimately to reduce risks associated to the movements of people and goods. We explore a data-driven approach to arrive at smart inspections of vehicles. Inspections are smart when they are performed (1) accurate, (2) automated, (3) fair, and (4) in an interpretable manner. We leverage tools from the network science and machine learning domain to encode the behavioral aspect of vehicle’s behavior. Tools used in this thesis include community detection, link prediction, and assortativity. We explore their applicability and provide technical methods. In the final chapter, we also discuss the matter of fairness in machine learning. Show less
Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections... Show moreSewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed. Instead of repairing sewer pipes when a problem becomes critical, such inspections allow municipalities to plan maintenance.Sewer pipe inspections are an attractive target for automation. While a potential improvement in terms of assessment quality and processing efficiency is generally promised by automation, in this case we would also decrease the variability which is a current problem. Besides the reasons for automating, the methods for automating are also attractive: a lot of (visual) data has been gathered over the past decades which may be used to train algorithms.This thesis compiles the results of five years of research into the possible automation of sewer pipe inspections with the tools of machine learning and computer vision. In this thesis, three distinct, yet complementary approaches to automating sewer pipe inspections are described:- Image-Based Unsupervised Anomaly Detection- Convolutional Neural Network Classification- Stereovision and Geometry Reconstruction Show less
Novel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel... Show moreNovel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel entities, leading to the formation of aggregated contamination. In this dissertation, we utilized advanced computational methods, such as molecular simulation, data mining, machine learning, and quantitative structure-activity relationship modeling. These methods were used to investigate the mechanisms of interaction between MNPs and other novel entities, the joint toxic action of MNPs and other novel entities, the factors affecting their joint toxicity to ecological species, as well as to quantitatively predict the interaction forces between MNPs and other novel entities, and the toxicity of their mixtures. The results indicate that understanding the mechanisms of interactions between novel entities and their modes of joint toxic action can provide an important theoretical basis for establishing effective risk assessment procedures to mitigate the effects of novel entities on ecosystems and human health. Furthermore, this dissertation provides important technical support and a practical basis for the quantitative prediction of the environmental behavior and toxicological effects of novel entities and their mixtures by applying various advanced in silico methods individually or in combination. Show less
This thesis looks at Artificial Intelligence (AI) and its potential to revolutionise the healthcare sector. The first part of this thesis focuses on the responsible development and validation of AI... Show moreThis thesis looks at Artificial Intelligence (AI) and its potential to revolutionise the healthcare sector. The first part of this thesis focuses on the responsible development and validation of AI-based clinical prediction algorithms, exploring the prime considerations in this process. The second part of this thesis addresses the opportunities for classical statistics and machine learning techniques for developing prediction algorithms. It also examines the performance, potential, and challenges of AI prediction algorithms for clinical practice. The conclusion states that cross-discipline collaboration, exchangeability of knowledge and results, and validation of AI for healthcare practice are essential for realising the potential of AI in healthcare. Show less
In this thesis, we examine various systems through the lens of several numerical methods. We delve into questions concerning thermalization in closed unitary systems, lattice gauge theories, and... Show moreIn this thesis, we examine various systems through the lens of several numerical methods. We delve into questions concerning thermalization in closed unitary systems, lattice gauge theories, and the intriguing properties of deep neural network phase spaces. Leveraging modern advancements in both software and hardware, we scrutinize these systems in greater detail, accessing previously unreachable regimes. Show less
This dissertation investigates the early recognition of persistent somatic symptoms (PSS) in primary care. A stepwise approach was used mapping the optimal methods for re-using primary care records... Show moreThis dissertation investigates the early recognition of persistent somatic symptoms (PSS) in primary care. A stepwise approach was used mapping the optimal methods for re-using primary care records for predictive modeling of PSS. This is important since up to 10% of the general population experiences PSS. Moreover, general practitioners (GPs) often encounter difficulties in recognizing PSS, which may delay adequate intervention, subsequently resulting in unnecessary high burden on the patient and health care system. The findings from this dissertation show that a complex interplay between factors from all biopsychosocial domains contribute to PSS-onset. Survey results show that GPs differ in their methods of PSS-registration. Many GPs indicate missing an unambiguous classification scheme and report needing more support, tools, and/or education for PSS-related consultations. Predictive modeling of different PSS-syndromes shows both overlapping and syndrome-specific predictors. Early predictive modeling of the broad spectrum of PSS shows moderate predictive accuracy based on seven approaches for candidate-predictor selection, including theory-driven and temporal and non-temporal data-driven approaches. In conclusion, this dissertation provides comprehensive evidence of the complexity of identification of PSS. Furthermore, it indicates that simple data-driven approaches could support PSS classification in primary care, although this should be combined with a multidisciplinary care approach. Show less
Radiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the... Show moreRadiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the presence of foreign objects. Computed tomography (CT) enables more accurate visualizations of an object in 3D, but requires more computation time. Spectral X-ray imaging is an important recent development to optimize these conflicting goals of speed and accuracy. This technique enables separation of detected X-ray photons in terms of energy. More information can be extracted from spectral images, which allows for better separation of materials. Deep learning is another important recent technique enabling machines to quickly carry out processing tasks, by training these with large volumes of data for these specific tasks.In this dissertation we present new processing methods that use spectral imaging and machine learning, with a special focus on industrial processes. We design a workflow using CT to efficiently generate large volumes of machine learning training data. In addition, we develop a compression method for efficient processing of large volumes of spectral data and two new spectral CT methods to produce more accurate reconstructions. The presented methods are designed for effective use in industry. Show less
The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.The goal is develop and assess... Show moreThe focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.The goal is develop and assess the technical methods which are required to shift the actions of the Inspectorate to a data-driven paradigm, concretely under a supervised classification framework of machine learning.The aspect of reliability is addressed as a data quality concern, viz. missingness and noise.The aspect of fairness is addressed as a counter to bias in the selection process of inspections.The conclusion is that, whilst no complete solution has yet been suggested, it is possible to address the concerns related to data quality and data bias, culminating in well-performing classification models which are reliable and fair. Show less
Stroke is one of the leading causes of disability and death worldwide. Prevention of stroke is therefore essential. Effective prevention should be tailored to the clinical characteristics,... Show moreStroke is one of the leading causes of disability and death worldwide. Prevention of stroke is therefore essential. Effective prevention should be tailored to the clinical characteristics, lifestyle, and environment of the individual, among others. This is also known as precision prevention. An important example illustrating the need for precision prevention is the existence of sex differences in stroke occurrence. In practice, for predicting stroke risk, only traditional risk factors (such as smoking and hypertension) are included, and women-specific risk factors are not yet routinely included. As a result, women with an increased risk of stroke may be missed, which also prevents timely initiation of preventive treatments. In this thesis, I tried to lay the foundation for precision prevention of stroke in women.Part I discussed the pathophysiology underlying women-specific risk factors for stroke, and gender differences in the clinical presentation of stroke. I found that the mechanisms underlying the relationship between women-specific risk factors and stroke, in particular the relationship between migraine and cerebral infarctions, seem to be particularly significant in the childbearing phase of life.In Part II, I described how health data from the EHR can be used to develop prediction models for the risk of myocardial infarction or stroke specifically for women under 50 years of age, and found that women-specific risk factors can add value in the predictions. However, there is still a long way to go to actually implement these models in practice, such as testing them on new datasets, and complying with current laws and regulations for safe application. Show less
The learning of software design is known to be a difficult and challenging task for students. This dissertation studies different didactic approaches for learning software design to improve the way... Show moreThe learning of software design is known to be a difficult and challenging task for students. This dissertation studies different didactic approaches for learning software design to improve the way we teach students software design. The research in the dissertation questions whether we can assess software design skills, what guidance is needed for the improvement of students’ understanding of software design and how to motivate and engage students for learning software design. The research explores the following: an instrument for measuring software design skills based on design principles, the gamification of learning software design, revealing students’ software design strategies, the use of peer-reflection for uncovering the difficulties students have during software design tasks, the use of teaching assistants as bridge between the lecturer and the students, the automation of grading software designs with machine learning, guiding feedback by a pedagogical agent and a workshop for engaging students into the process of software development. The research contributes to the future education of software design. Show less
Inverse problems are problems where we want to estimate the values of certain parameters of a system given observations of the system. Such problems occur in several areas of science and... Show moreInverse problems are problems where we want to estimate the values of certain parameters of a system given observations of the system. Such problems occur in several areas of science and engineering. Inverse problems are often ill-posed, which means that the observations of the system do not uniquely define the parameters we seek to estimate, or that the solution is highly sensitive to small changes in the observation. In order to solve such problems, therefore, we need to make use of additional knowledge about the system at hand. One such prior information is given by the notion of sparsity. Sparsity refers to the knowledge that the solution to the inverse problem can be expressed as a combination of a few terms. The sparsity of a solution can be controlled explicitly or implicitly. An explicit way to induce sparsity is to minimize the number of non-zero terms in the solution. Implicit use of sparsity can be made, for e.g., by making adjustments to the algorithm used to arrive at the solution.In this thesis we studied various inverse problems that arise in different application areas, such as tomographic imaging and equation learning for biology, and showed how ideas of sparsity can be used in each case to design effective algorithms to solve such problems. Show less
The societal burden of spinal conditions is vast and continues to grow with the in- creasing prevalence of patients with spinal degenerative disease, spinal metasta- ses, and spinal infections.... Show moreThe societal burden of spinal conditions is vast and continues to grow with the in- creasing prevalence of patients with spinal degenerative disease, spinal metasta- ses, and spinal infections. Recent application of artificial intelligence in healthcare have shown great promise and similar extensions in spine surgery may improve decision-making. The purpose of this thesis was to examine the utility of predictive analytics and natural language processing in spine surgery. Show less
The aim of this thesis is to determine diagnostic performance of machine learning in differentiating between atypical cartilaginous tumor (ACT) and high-grade chondrosarcoma (CS) based on radiomic... Show moreThe aim of this thesis is to determine diagnostic performance of machine learning in differentiating between atypical cartilaginous tumor (ACT) and high-grade chondrosarcoma (CS) based on radiomic features derived from magnetic resonance imaging (MRI) and computed tomography (CT). In chapter 2, the concept of radiomics of musculoskeletal sarcomas is introduced and a systematic review on radiomic feature reproducibility and validation strategies is conducted. In chapter 3, a preliminary study is performed to investigate the performance of MRI radiomics-based machine learning in discriminating ACT from high-grade CS, using a single-center cohort, in comparison with an expert radiologist. In chapter 4, the influence of interobserver segmentation variability on the reproducibility of CT and MRI radiomic features of cartilaginous bone tumors is assessed. In chapter 5, the performance of CT radiomics-based machine learning in discriminating ACT from high-grade CS of long bones is determined and validated using independent data from a multicenter cohort, compared to an expert radiologist. In chapter 6, the performance of MRI radiomics-based machine learning in differentiating between ACT and grade II CS of long bones is determined and validated using independent data from a multicenter cohort, in comparison with an expert radiologist. Finally, in chapter 7, the main results and implications of this thesis are summarized and discussed. Show less
Despite improved surgical and adjuvant treatment options, malignant brain tumors remain non-curable to date. The thin line between treatment effectiveness and patient harms underpins the importance... Show moreDespite improved surgical and adjuvant treatment options, malignant brain tumors remain non-curable to date. The thin line between treatment effectiveness and patient harms underpins the importance of tailoring clinical management to the individual brain tumor patient. Over the past decades, the volume and complexity of clinically-derived patient data (i.e., imaging, genomics, free-text etc.) is increasing exponentially. Machine learning provides a vast range of algorithms that can learn from this data and guide clinical decision-making by providing accurate patient-level predictions. The current thesis describes several studies along the continuum of the machine learning spectrum as it applies to neurosurgical oncology. Part I investigates postoperative complications and risk factors in patients operated for a primary malignant brain tumor. Part II describes de development of a model for the prediction of individual-patient survival in glioblastoma patients. Part III encompasses the development of a natural language processing framework for automated medical text analysis. Machine learning algorithms should be considered as an extension to statistical approaches and exist along a continuum determined by how much is specified by humans and how much is learnt by the machine. Although machine learning algorithms can produce highly accurate predictions based on high-dimensional data, clinicians and researchers should interpret the clinical implications of these predictions on case-by-case basis. Show less
Image registration is the process of aligning images by finding the spatial relation between the images. Assuming two images called fixed and moving images are taken at different time, different... Show moreImage registration is the process of aligning images by finding the spatial relation between the images. Assuming two images called fixed and moving images are taken at different time, different spatial location, or via a different imaging technique, the aim of image registration is to find an optimal transformation that aligns the fixed and the moving images. Performing an automatic fast image registration with less manual finetuning can speed up numerous medical image processing procedures. In addition, an automatic quality assessment of registration can speed up this time-consuming task. In this thesis, we developed a fast learning-based image registration technique called RegNet.Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, quantitative error prediction can be helpful in improving the registration quality. In this thesis, we proposed two quality assessment mechanisms using random forests (RF) and convolutional long short term memory (ConvLSTM), in which the latter performs faster and more accurate. Show less
