BackgroundCardiotoxicity, characterized by severe cardiac dysfunction, is a major problem in patients treated with different classes of anticancer drugs. Development of predictable human-based... Show moreBackgroundCardiotoxicity, characterized by severe cardiac dysfunction, is a major problem in patients treated with different classes of anticancer drugs. Development of predictable human-based models and assays for drug screening are crucial for preventing potential drug-induced adverse effects. Current animal in vivo models and cell lines are not always adequate to represent human biology. Alternatively, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) show great potential for disease modelling and drug-induced toxicity screenings. Fully automated high-throughput screening of drug toxicity on hiPSC-CMs by fluorescence image analysis is, however, very challenging, due to clustered cell growth patterns and strong intracellular and intercellular variation in the expression of fluorescent markers.ResultsIn this paper, we report on the development of a fully automated image analysis system for quantification of cardiotoxic phenotypes from hiPSC-CMs that are treated with various concentrations of anticancer drugs doxorubicin or crizotinib. This high-throughput system relies on single-cell segmentation by nuclear signal extraction, fuzzy C-mean clustering of cardiac α-actinin signal, and finally nuclear signal propagation. When compared to manual segmentation, it generates precision and recall scores of 0.81 and 0.93, respectively.ConclusionsOur results show that our fully automated image analysis system can reliably segment cardiomyocytes even with heterogeneous α-actinin signals. Show less
By training with virtual opponents known as computer generated forces (CGFs), trainee fighter pilots can build the experience necessary for air combat operations, at a fraction of the cost of... Show moreBy training with virtual opponents known as computer generated forces (CGFs), trainee fighter pilots can build the experience necessary for air combat operations, at a fraction of the cost of training with real aircraft. In practice however, the variety of CGFs is not as wide as it can be. This is largely due to a lack of behaviour models for the CGFs. In this thesis we investigate to what extent behaviour models for the CGFs in air combat training simulations can be automatically generated, by the use of machine learning.The domain of air combat is complex, and machine learning methods that operate within this domain must be suited to the challenges posed by the domain. Our research shows that the dynamic scripting algorithm greatly facilitates the automatic generation of air combat behaviour models, while being sufficiently flexible to be moulded into answers to the challenges. However, ensuring the validity of the newly generated behaviour models remains to be a point of attention for future research. Show less
Heuvel, L. van den; Dorsey, R.R.; Prainsack, B.; Post, B.; Stiggelbout, A.M.; Meinders, M.J.; Bloem, B.R. 2020
Clinical decision making for Parkinson's disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the... Show moreClinical decision making for Parkinson's disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the personal needs and preferences of patients. All three sources have clear value but also share several important limitations, mainly regarding subjectivity, generalizability and variability. For example, current scientific evidence, especially from controlled clinical trials, is often based on selected study populations, making it difficult to translate the outcome to the care for individual patients in everyday clinical practice. Big data, including data from real-life unselected Parkinson populations, can help to bridge this information gap. Fine-grained patient profiles created from big data have the potential to aid in identifying therapeutic approaches that will be most effective given each patient's individual characteristics, which is particularly important for a disorder characterized by such tremendous interindividual variability as Parkinson's disease. In this viewpoint, we argue that big data approaches should be acknowledged and harnessed, not to replace existing information resources, but rather as a fourth and complimentary source of information in clinical decision making, helping to represent the full complexity of individual patients. We introduce the `quadruple decision making' model and illustrate its mode of action by showing how this can be used to pursue precision medicine for persons living with Parkinson's disease. Show less
OBJECTIVE Nonroutine discharge after elective spine surgery increases healthcare costs, negatively impacts patient satisfaction, and exposes patients to additional hospital-acquired complications.... Show moreOBJECTIVE Nonroutine discharge after elective spine surgery increases healthcare costs, negatively impacts patient satisfaction, and exposes patients to additional hospital-acquired complications. Therefore, prediction of nonroutine discharge in this population may improve clinical management. The authors previously developed a machine learning algorithm from national data that predicts risk of nonhome discharge for patients undergoing surgery for lumbar disc disorders. In this paper the authors externally validate their algorithm in an independent institutional population of neurosurgical spine patients.METHODS Medical records from elective inpatient surgery for lumbar disc herniation or degeneration in the Transitional Care Program at Brigham and Women's Hospital (2013-2015) were retrospectively reviewed. Variables included age, sex, BMI, American Society of Anesthesiologists (ASA) class, preoperative functional status, number of fusion levels, comorbidities, preoperative laboratory values, and discharge disposition. Nonroutine discharge was defined as postoperative discharge to any setting other than home. The discrimination (c-statistic), calibration, and positive and negative predictive values (PPVs and NPVs) of the algorithm were assessed in the institutional sample.RESULTS Overall, 144 patients underwent elective inpatient surgery for lumbar disc disorders with a nonroutine discharge rate of 6.9% (n = 10). The median patient age was 50 years and 45.1% of patients were female. Most patients were ASA class II (66.0%), had 1 or 2 levels fused (80.6%), and had no diabetes (91.7%). The median hematocrit level was 41.2%. The neural network algorithm generalized well to the institutional data, with a c-statistic (area under the receiver operating characteristic curve) of 0.89, calibration slope of 1.09, and calibration intercept of -0.08. At a threshold of 0.25, the PPV was 0.50 and the NPV was 0.97.CONCLUSIONS This institutional external validation of a previously developed machine learning algorithm suggests a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge. Performance in the institutional cohort was comparable to performance in the derivation cohort and represents an improved predictive value over clinician intuition. This finding substantiates initial use of this algorithm in clinical practice. This tool may be used by multidisciplinary teams of case managers and spine surgeons to strategically invest additional time and resources into postoperative plans for this population. Show less
Dementie is een verwoestende ziekte waar wereldwijd miljoenen mensen aan leiden. De meest voorkomende oorzaak van dementie is de ziekte van Alzheimer. Voor het ontwikkelen van effectieve... Show moreDementie is een verwoestende ziekte waar wereldwijd miljoenen mensen aan leiden. De meest voorkomende oorzaak van dementie is de ziekte van Alzheimer. Voor het ontwikkelen van effectieve behandelingen is het belangrijk om dementie in een vroeg stadium te detecteren. Traditioneel alzheimeronderzoek is voornamelijk gericht op groepsverschillen tussen patiënten en controles. Recent onderzoek is deels verschoven naar individuele classificatie met machine learning. In dit proefschrift onderzoeken we het gebruik van magnetic resonance imaging (MRI) voor automatische detectie van de ziekte van Alzheimer, en vroege detectie van cognitieve achteruitgang. In dit proefschrift laten we zien dat het combineren van MRI modaliteiten de classificatie kan verbeteren. Ook laten we zien dat diffusie MRI een goede maat is om alzheimer te diagnosticeren. Bij toepassing van dezelfde methoden op een groep presymptomatische gendragers die amyloïdangiopathie zullen ontwikkelen vonden we geen verschillen tussen de gendragers en controles. Tevens waren we niet in staat om cognitieve achteruitgang na 4 jaar te voorspellen in een groep ouderen met verhoogd risico op achteruitgang. Met MRI kunnen betrouwbare individuele uitspraken gedaan kan worden over patiënten, maar het is met de huidige methoden niet gevoelig voor vroege detectie van cognitieve achteruitgang. Show less
Abdelaal, T.; Unen, V. van; Hollt, T.; Koning, F.; Reinders, M.J.T.; Mahfouz, A. 2019
Cardiovascular computed tomography (CCT) has undergone rapid maturation over the last decade and is now of proven clinical utility in the diagnosis and management of coronary artery disease, in... Show moreCardiovascular computed tomography (CCT) has undergone rapid maturation over the last decade and is now of proven clinical utility in the diagnosis and management of coronary artery disease, in guiding structural heart disease intervention, and in the diagnosis and treatment of congenital heart disease. The next decade will undoubtedly witness further advances in hardware and advanced analytics that will potentially see an increasingly core role for CCT at the center of clinical cardiovascular practice. In coronary artery disease assessment this may be via improved hemodynamic adjudication, and shear stress analysis using computational flow dynamics, more accurate and robust plaque characterization with spectral or photon- counting CT, or advanced quantification of CT data via artificial intelligence, machine learning, and radiomics. In structural heart disease, CCT is already pivotal to procedural planning with adjudication of gradients before and following intervention, whereas in congenital heart disease CCT is already used to support clinical decision making from neonates to adults, often with minimal radiation dose. In both these areas the role of computational flow dynamics, advanced tissue printing, and image modelling has the potential to revolutionize the way these complex conditions are managed, and CCT is likely to become an increasingly critical enabler across the whole advancing field of cardiovascular medicine. (c) 2019 by the American College of Cardiology Foundation. Show less
Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly... Show moreDeep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper. we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems. Show less
