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Systematic reviews are a cornerstone of today's evidence-informed decision making. With the rapid expansion of questions to be addressed and scientific information produced, there is a growing workload on reviewers, making the current practice unsustainable without the aid of automation tools. While many automation tools have been developed and are available, uptake seems to be lagging. For this reason, we set out to investigate the current level of uptake and what the potential barriers and facilitators are for the adoption of automation tools in systematic reviews. We deployed surveys among systematic reviewers that gathered information on tool uptake, demographics, systematic review characteristics, and barriers and facilitators for uptake. Systematic reviewers from multiple domains were targeted during recruitment; however, responders were predominantly from the biomedical sciences. We found that automation tools are currently not widely used among the participants. When tools are used, participants mostly learn about them from their environment, for example, through colleagues, peers, or organization. Tools are often chosen on the basis of user experience, either by own experience or from colleagues or peers. Lastly, licensing, steep learning curve, lack of support, and mismatch to workflow are often reported by participants as relevant barriers. While conclusions can only be drawn for the biomedical field, our work provides evidence and confirms the conclusions and recommendations of previous work, which was based on expert opinions. Furthermore, our study highlights the importance that organizations and best practices in a field can have for the uptake of automation tools for systematic reviews.

The sustainability of science gateways has been a topic of active discussion because they have been created and supported in the context of temporary research and infrastructure projects. As successful projects come to an end, it is necessary to find (new) models to secure continuous exploitation of products generated by these projects. Taking this step requires business considerations that are not trivial to do from the role of a researcher. This paper presents our experiences in adopting a methodology from lean business development, the Business Model Canvas (BMC). This methodology enables structured reflection upon the business model and facilitates exploring alternative ones (pivoting). We have applied the BMC to one of the science gateways designed, developed, and operated by the Academic Medical Center (AMC) e-Science group: the AMC Computational Neuroscience Gateway. The current gateway BMC is explained in the paper and used as basis for a reflection to improve its sustainability. Alternative business models are given as examples of BMC iteration or pivots. This exercise helped us to structure the various aspects to be considered when designing or reflecting upon the business model of our gateway. It also facilitated the visualization of the complete business picture and helps the reflection about improvements in the business model toward sustainability. We believe that this methodology could be valuable also for the reflection about sustainability of other science gateways that are growing from academic groups that do not have business training. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• Quantification of EEG reactivity (EEG-R) using machine learning predicts poor outcome after cardiac arrest as good as visual analysis of EEG-R. • Quantitative analysis of EEG background at 24 h alone yields higher prognostic value than quantitative analysis of EEG-R. • Quantitative analysis of EEG-R and EEG background combined reduces the number of false positives. To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA). Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3–5 within 6 months. The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80–86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40–51) and 89% specificity (95%-CI 86–92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83–88) at 24 h after CA, with 62% sensitivity (95%-CI 57–67) and 84% specificity (95%-CI 79–88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives. Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data. Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data. [ABSTRACT FROM AUTHOR]

Background: Acute stroke care demands fast procedures performed through the collaboration of multiple professionals across multiple organizations. Cloud computing and the wide adoption of electronic medical records (EMRs) enable health care systems to improve data availability and facilitate sharing among professionals. However, designing a secure and privacy-preserving EMR cloud-based application is challenging because it must dynamically control the access to the patient's EMR according to the needs for data during treatment., Objective: We developed a prototype of a secure EMR cloud-based application. The application explores the security features offered by the eHealth cloud-based framework created by the Advanced Secure Cloud Encrypted Platform for Internationally Orchestrated Solutions in Health Care Horizon 2020 project. This study aimed to collect impressions, challenges, and improvements for the prototype when applied to the use case of secure data sharing among acute care teams during emergency treatment in the Netherlands., Methods: We conducted 14 semistructured interviews with medical professionals with 4 prominent roles in acute care: emergency call centers, ambulance services, emergency hospitals, and general practitioner clinics. We used in-depth interviews to capture their perspectives about the application's design and functions and its use in a simulated acute care event. We used thematic analysis of interview transcripts. Participants were recruited until the collected data reached thematic saturation., Results: The participants' perceptions and feedback are presented as 5 themes identified from the interviews: current challenges (theme 1), quality of the shared EMR data (theme 2), integrity and auditability of the EMR data (theme 3), usefulness and functionality of the application (theme 4), and trust and acceptance of the technology (theme 5). The results reinforced the current challenges in patient data sharing during acute stroke care. Moreover, from the user point of view, we expressed the challenges of adopting the Advanced Secure Cloud Encrypted Platform for Internationally Orchestrated Solutions in Health Care Acute Stroke Care application in a real scenario and provided suggestions for improving the proposed technology's acceptability., Conclusions: This study has endorsed a system that supports data sharing among acute care professionals with efficiency, but without compromising the security and privacy of the patient. This explorative study identified several significant barriers to and improvement opportunities for the future acceptance and adoption of the proposed system. Moreover, the study results highlight that the desired digital transformation should consider integrating the already existing systems instead of requesting migration to a new centralized system., (©Marcela Tuler de Oliveira, Lúcio Henrik Amorim Reis, Henk Marquering, Aeilko H Zwinderman, Sílvia Delgado Olabarriaga. Originally published in JMIR Formative Research (https://formative.jmir.org), 23.12.2022.)

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing., (©Moritz Platt, Anton Hasselgren, Juan Manuel Román-Belmonte, Marcela Tuler de Oliveira, Hortensia De la Corte-Rodríguez, Sílvia Delgado Olabarriaga, E Carlos Rodríguez-Merchán, Tim Ken Mackey. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 06.04.2021.)

Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role in treatment selection and prognosis. Radiological biomarkers require expert annotation and are subject to inter-observer variability. Recently, Deep Learning has been introduced to reproduce these radiological image biomarkers. Instead of reproducing these biomarkers, in this work, we investigated Deep Learning techniques for building models to directly predict good reperfusion after endovascular treatment (EVT) and good functional outcome using CT angiography images. These models do not require image annotation and are fast to compute. We compare the Deep Learning models to Machine Learning models using traditional radiological image biomarkers. We explored Residual Neural Network (ResNet) architectures, adapted them with Structured Receptive Fields (RFNN) and auto-encoders (AE) for network weight initialization. We further included model visualization techniques to provide insight into the network's decision-making process. We applied the methods on the MR CLEAN Registry dataset with 1301 patients. The Deep Learning models outperformed the models using traditional radiological image biomarkers in three out of four cross-validation folds for functional outcome (average AUC of 0.71) and for all folds for reperfusion (average AUC of 0.65). Model visualization showed that the arteries were relevant features for functional outcome prediction. The best results were obtained for the ResNet models with RFNN. Auto-encoder initialization often improved the results. We concluded that, in our dataset, automated image analysis with Deep Learning methods outperforms radiological image biomarkers for stroke outcome prediction and has the potential to improve treatment selection., (Copyright © 2019. Published by Elsevier Ltd.)

Systematic reviews are a cornerstone of today's evidence-informed decision making. With the rapid expansion of questions to be addressed and scientific information produced, there is a growing workload on reviewers, making the current practice unsustainable without the aid of automation tools. While many automation tools have been developed and are available, uptake seems to be lagging. For this reason, we set out to investigate the current level of uptake and what the potential barriers and facilitators are for the adoption of automation tools in systematic reviews. We deployed surveys among systematic reviewers that gathered information on tool uptake, demographics, systematic review characteristics, and barriers and facilitators for uptake. Systematic reviewers from multiple domains were targeted during recruitment; however, responders were predominantly from the biomedical sciences. We found that automation tools are currently not widely used among the participants. When tools are used, participants mostly learn about them from their environment, for example, through colleagues, peers, or organization. Tools are often chosen on the basis of user experience, either by own experience or from colleagues or peers. Lastly, licensing, steep learning curve, lack of support, and mismatch to workflow are often reported by participants as relevant barriers. While conclusions can only be drawn for the biomedical field, our work provides evidence and confirms the conclusions and recommendations of previous work, which was based on expert opinions. Furthermore, our study highlights the importance that organizations and best practices in a field can have for the uptake of automation tools for systematic reviews., (© 2018 John Wiley & Sons, Ltd.)

Background: Major depressive disorder (MDD) is characterized by abnormalities in both brain structure and function within a frontolimbic network. However, little is known about the relation between structural and functional abnormalities in MDD. Here, we used a multimodal neuroimaging approach to investigate the relation between structural connectivity and functional connectivity within the frontolimbic network., Methods: Eighteen MDD and 24 healthy control subjects were included, of which the integrity of the uncinate fasciculus was assessed that connects the subgenual anterior cingulate cortex (ACC) to the medial temporal lobe (MTL) with diffusion tensor imaging. Furthermore, we assessed the functional connectivity between these brain regions with functional magnetic resonance imaging., Results: The results showed that white matter integrity of the uncinate fasciculus was reduced and that functional connectivity between the subgenual ACC and MTL was enhanced in MDD. Importantly, we identified a negative correlation between uncinate fasciculus integrity and subgenual ACC functional connectivity with the bilateral hippocampus in MDD but not in healthy control subjects. Moreover, this negative structure-function relation in MDD was positively associated with depression severity., Conclusions: These findings suggest that structural abnormalities in MDD are associated with increased functional connectivity between subgenual ACC and MTL and that these changes are concomitant with severity of depressive symptoms. This association indicates that structural abnormalities in MDD contribute to increased functional connectivity within the frontolimbic network., (Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Measuring the diffusion properties of crossing fibers is very challenging due to the high number of model parameters involved and the intrinsically low SNR of Diffusion Weighted MR Images. Noise filtering aims at suppressing the noise while pertaining the data distribution. We propose an adaptive version of the Linear Minimum Mean Square Error (LMMSE) estimator to achieve this. Our filter applies an adaptive filtering kernel that is based on a space-variant estimate of the noise level and a weight consisting of the product of a Gaussian kernel and the diffusion similarity with respect to the central voxel. The experiments show that the data distribution after filtering is still Rician and that the diffusivity values are estimated with a higher precision while pertaining an equal accuracy. We demonstrate on brain data that our adaptive approach performs better than the initial LMMSE estimator.

Segmentation of the object of interest is a difficult step in the analysis of digital images. Fully automatic methods sometimes fail, producing incorrect results and requiring the intervention of a human operator. This is often true in medical applications, where image segmentation is particularly difficult due to restrictions imposed by image acquisition, pathology and biological variation. In this paper we present an early review of the largely unknown territory of human-computer interaction in image segmentation. The purpose is to identify patterns in the use of interaction and to develop qualitative criteria to evaluate interactive segmentation methods. We discuss existing interactive methods with respect to the following aspects: the type of information provided by the user, how this information affects the computational part, and the purpose of interaction in the segmentation process. The discussion is based on the potential impact of each strategy on the accuracy, repeatability and interaction efficiency. Among others, these are important aspects to characterise and understand the implications of interaction to the results generated by an interactive segmentation method. This survey is focused on medical imaging, however similar patterns are expected to hold for other applications as well.