Background: Where it has been determined that a resident in a nursing home living with dementia loses decisional capacity, nursing home staff must deliver care that is in the person's best... Show moreBackground: Where it has been determined that a resident in a nursing home living with dementia loses decisional capacity, nursing home staff must deliver care that is in the person's best interests. Ideally, decisions should be made involving those close to the person, typically a family carer and health and social care providers. The aim of the Family Carer Decisional Support intervention is to inform family carers on end-of-life care options for a person living with advanced dementia and enable them to contribute to advance care planning. This implementation study proposes to; 1) adopt and apply the intervention internationally; and, 2) train nursing home staff to deliver the family carer decision support intervention. Methods: This study will employ a multiple case study design to allow an understanding of the implementation process and to identify the factors which determine how well the intervention will work as intended. We will enrol nursing homes from each country (Canada n = 2 Republic of Ireland = 2, three regions in the UK n = 2 each, The Netherlands n = 2, Italy n = 2 and the Czech Republic n = 2) to reflect the range of characteristics in each national and local context. The RE-AIM (reach, effectiveness, adoption, implementation, maintenance) framework will guide the evaluation of implementation of the training and information resources. Our mixed methods study design has three phases to (1) establish knowledge about the context of implementation, (2) participant baseline information and measures and (3) follow up evaluation. Discussion: The use of a multiple case study design will enable evaluation of the intervention in different national, regional, cultural, clinical, social and organisational contexts, and we anticipate collecting rich and in-depth data. While it is hoped that the intervention resources will impact on policy and practice in the nursing homes that are recruited to the study, the development of implementation guidelines will ensure impact on wider national policy and practice. It is our aim that the resources will be sustainable beyond the duration of the study and this will enable the resources to have a longstanding relevance for future advance care planning practice for staff, family carers and residents with advanced dementia. Show less
Education is important for fostering research integrity (RI). Although RI training is increasingly provided, there is little knowledge on how research stakeholders view institutional RI education... Show moreEducation is important for fostering research integrity (RI). Although RI training is increasingly provided, there is little knowledge on how research stakeholders view institutional RI education and training policies. Following a constructivist approach, we present insights about research stakeholders’ views and experiences regarding how research institutions can develop and implement RI education and training policies. We conducted thirty focus groups, engaging 147 participants in eight European countries. Using a mixed deductive-inductive thematic analysis, we identified five themes: (1) RI education should be available to all; (2) education and training approaches and goals should be tailored; (3) motivating trainees is essential; (4) both formal and informal educational formats are necessary; and (5) institutions should take into account various individual, institutional, and system-of-science factors when implementing RI education. Our findings suggest that institutions should make RI education attractive for all and tailor training to disciplinary-specific contexts. Show less
Yin, Z.; Geraedts, V.J.; Wang, Z.Q.; Contarino, M.F.; Dibeklioglu, H.; Gemert, J. van 2022
Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is... Show moreParkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism. Seven tasks from the Movement Disorders Society - Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification. Show less
Background and Purpose: Two medical specialties, general surgery and orthopaedic surgery, with different training programs but matching trauma certification requirements, provide hip fracture... Show moreBackground and Purpose: Two medical specialties, general surgery and orthopaedic surgery, with different training programs but matching trauma certification requirements, provide hip fracture surgery in the Netherlands. This study analyses treatment preferences and guideline adherence of Dutch surgeons with different surgical backgrounds.Patients and Methods: All hip fracture patients registered in the Dutch Hip Fracture Audit in 2018 and 2019 were included in this retrospective study. Four types of surgeons were distinguished: trauma-certified general surgeons (ST+), non-trauma certified general surgeons (ST-), trauma-certified orthopaedic surgeons (OT+) and non-trauma certified orthopaedic surgeons (OT-). Differences in patient characteristics, and practice variation in treatment choices and guideline adherence per fracture type were analysed using descriptive statistics.Results: 28,656 patients were included; 16,367 (57.1%) treated by ST +, 1,371 (4.8%) by ST-, 4,692 (16.4%) by OT+ and 6,226 (21.7%) by OT-. Few clinically relevant differences in patient characteristics and hospital processes were found between all surgeon groups. Displaced FNF were the most commonly treated fracture type for all types of surgeons. Both OT+ and OT- operated mostly (displaced) FNFs, while the fracture types treated by ST+ and ST- were more heterogeneous. For all fracture types, the orthopaedic surgeons performed THA and HA more often than general surgeons, while general surgeons more often placed SHS and IMN for specific fracture types. Guideline adherence was on average 68.4% and differed significantly per surgeon type (68.7% by ST+, 65.2% by ST-, 74.4% by OT+ and 63.6% by OT-(p<0.01)), as well as per fracture type: >90% treatment according to the guideline for trochanteric AO-31A2 and A3 fractures, 18.8% for AO-31A1 fractures and 51.7% guideline adherence for undisplaced FNF. Guideline adherence for displaced FNF varied depending on patient characteristics.Discussion: In the Netherlands, different surgical specialists treat different types of hip fractures and have different preferences concerning implants for hip fracture surgery in comparable patients. Guideline adherence of trauma- and non-trauma certified orthopaedics and general surgeons differs significantly. Reduction of practice variation should be strived for in order to improve hip fracture care. (C) 2021 The Authors. Published by Elsevier Ltd. Show less
Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this article, we propose to combine deep neural networks (DNN) with mathematics-guided... Show moreEmbedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this article, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods. Code is available at https://github.com/tao-aimi/DeepRecursiveEmbedding. Show less
Dell'Oglio, P.; Turri, F.; Larcher, A.; D'Hondt, F.; Sanchez-Salas, R.; Bochner, B.; ... ; YAU Working Grp Robot-Assisted Sur 2022
Robot-assisted radical cystectomy (RARC) continues to expand, and several surgeons start training for this complex procedure. This calls for the development of a structured training program, with... Show moreRobot-assisted radical cystectomy (RARC) continues to expand, and several surgeons start training for this complex procedure. This calls for the development of a structured training program, with the aim to improve patient safety during RARC learning curve. A modified Delphi consensus process was started to develop the curriculum structure. An online survey based on the available evidence was delivered to a panel of 28 experts in the field of RARC, selected according to surgical and research experience, and expertise in running training courses. Consensus was defined as >80% agreement between the responders. Overall, 96.4% experts completed the survey. The structure of the RARC curriculum was defined as follows: (1) theoretical training; (2) preclinical simulation-based training: 5-d simulation-based activity, using models with increasing complexity (ie, virtual reality, and dry-and wet-laboratory exercises), and nontechnical skills training session; (3) clinical training: modular console activity of at least 6 mo at the host center (a RARC case was divided into 11 steps and steps of similar complexity were grouped into five modules); and (4) final evaluation: blind review of a video-recorded RARC case. This structured training pathway will guide a starting surgeon from the first steps of RARC toward independent completion of a full procedure. Clinical implemen-tation is urgently needed. Patient summary: Robot-assisted radical cystectomy (RARC) is a complex procedure. The first structured training program for RARC was developed with the goal of aiding surgeons to overcome the learning curve of this procedure, improving patients' safety at the same time. (c) 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved. Show less
Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical... Show moreManual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality. For this purpose, we present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs). The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention gates in each dense block to selectively concentrate on determinant feature maps and regions. Dilated convolutional layers are used to manage GPU memory and increase the network receptive field. We collected a dataset of 792 scans from 288 distinct patients including varying anatomies with air pockets, feeding tubes and proximal tumors. Repeatability and reproducibility studies were conducted for three distinct splits of training and validation sets. The proposed network achieved a DSC value of 0.79 +/- 0.20, a mean surface distance of 5.4 +/- 20.2mm and 95% Hausdorff distance of 14.7 +/- 25.0mm for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone. Our code is publicly available via https://github.com/yousefis/DenseUnet_Esophagus_Segmentation. Show less
Elmahdy, M.S.; Beljaards, L.; Yousefi, S.; Sokooti, H.; Verbeek, F.; Heide, U.A. van der; Staring, M. 2021
Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them... Show moreMedical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information. We propose to merge these tasks not only on the loss level, but on the architectural level as well. We studied this approach in the context of adaptive image-guided radiotherapy for prostate cancer, where planning and follow-up CT images as well as their corresponding contours are available for training. At testing time the contours of the follow-up scans are not available, which is a common scenario in adaptive radiotherapy. The study involves two datasets from different manufacturers and institutes. The first dataset was divided into training (12 patients) and validation (6 patients), and was used to optimize and validate the methodology, while the second dataset (14 patients) was used as an independent test set. We carried out an extensive quantitative comparison between the quality of the automatically generated contours from different network architectures as well as loss weighting methods. Moreover, we evaluated the quality of the generated deformation vector field (DVF). We show that MTL algorithms outperform their Single-Task Learning (STL) counterparts and achieve better generalization on the independent test set. The best algorithm achieved a mean surface distance of 1.06 +/- 0.3 mm, 1.27 +/- 0.4 mm, 0.91 +/- 0.4 mm, and 1.76 +/- 0.8 mm on the validation set for the prostate, seminal vesicles, bladder, and rectum, respectively. The high accuracy of the proposed method combined with the fast inference speed, makes it a promising method for automatic re-contouring of follow-up scans for adaptive radiotherapy, potentially reducing treatment related complications and therefore improving patients quality-of-life after treatment. The source code is available at https://github.com/moelmahdy/JRS-MTL. Show less
Advanced endoscopic imaging is an emerging field in endoscopy practice, especially in optical diagnosis. Current technologies like virtual chromoendoscopy and small-field technologies allow... Show moreAdvanced endoscopic imaging is an emerging field in endoscopy practice, especially in optical diagnosis. Current technologies like virtual chromoendoscopy and small-field technologies allow visualization of subtle changes in mucosal and vascular patterns that are predictive of histology. The limiting factor in broadly utilizing these techniques is training and the need for reliable detection of these subtleties. This review provides the current evidence and limitations of training in advanced endoscopic imaging, and future directions of learning. A literature search was performed on PubMed and Medline through March 2020 with relevant keywords as advanced endoscopic imaging, training, and learning. References of relevant articles were screened for additional literature. Several didactic and web-based education programs are developed for training in virtual chromoendoscopy, autofluorescence imaging, confocal laser endomicroscopy, and volumetric laser endomicroscopy. Studies and post-hoc analysis on learning curves showed relatively steep learning curves after training, and web-based education seems to be as valuable as in-person didactic training for most techniques. However, consistent performance on expert level after training has not yet been demonstrated. Most advanced endoscopic imaging techniques are learned within a reasonable timeframe. Future efforts to enhance training and implementation of these techniques should focus on developing standardized and broadly incorporated training programs. The future role of artificial intelligence-assistance in advanced endoscopy and training has to be elucidated. Show less
Palagonia, E.; Mazzone, E.; Naeyer, G. de; D'Hondt, F.; Collins, J.; Wisz, P.; ... ; Dell'Oglio, P. 2020
Purpose To assess the available literature evidence that discusses the effect of surgical experience on patient outcomes in robotic setting. This information is used to help understand how we can... Show morePurpose To assess the available literature evidence that discusses the effect of surgical experience on patient outcomes in robotic setting. This information is used to help understand how we can develop a learning process that allows surgeons to maximally accommodate patient safety. Methods A literature search of the MEDLINE/PubMed and Scopus database was performed. Original and review articles published in the English language were included after an interactive peer-review process of the panel. Results Robotic surgical procedures require high level of experience to guarantee patient safety. This means that, for some procedures, the learning process might be longer than originally expected. In this context, structured training programs that assist surgeons to improve outcomes during their learning processes were extensively discussed. We identified few structured robotic curricula and demonstrated that for some procedures, curriculum trained surgeons can achieve outcomes rates during their initial learning phases that are at least comparable to those of experienced surgeons from high-volume centres. Finally, the importance of non-technical skills on patient safety and of their inclusion in robotic training programs was also assessed. Conclusion To guarantee safe robotic surgery and to optimize patient outcomes during the learning process, standardized and validated training programs are instrumental. To date, only few structured validated curricula exist for standardized training and further efforts are needed in this direction. Show less
Pezzotti, N.; Yousefi, S.; Elmahdy, M.S.; Gemert, J.H.F. van; Schuelke, C.; Doneva, M.; ... ; Staring, M. 2020
Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR... Show moreAdaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the 8x accelerated multi-coil, the 4x multi-coil, and the 4x single-coil tracks. This demonstrates the superior performance and wide applicability of the method. Show less
Objective: To develop a consensus-based set of generic competencies in antimicrobial prescribing and stewardship for European prescribers through a structured consensus procedure.Methods: The RAND... Show moreObjective: To develop a consensus-based set of generic competencies in antimicrobial prescribing and stewardship for European prescribers through a structured consensus procedure.Methods: The RAND-modified Delphi procedure comprised two online questionnaire rounds, a face-to-face meeting between rounds, and a final review. Our departure point was a set of competencies agreed previously by consensus among a UK multi-disciplinary panel, and which had been subsequently revised through consultation with ESCMID Study Group representatives. The 46 draft competency points were reviewed by an expert panel consisting of specialists in infectious diseases and clinical microbiology, and pharmacists. Each proposed competency was assessed using a nine-point Likert scale, for relevance as a minimum standard for all independent prescribers in all European countries.Results: A total of 65 expert panel members participated, from 24 European countries (one to six experts per country). There was very high satisfaction (98%) with the final competencies set, which included 35 competency points, in three sections: core concepts in microbiology, pathogenesis and diagnosing infections (11 points); antimicrobial prescribing (20 points); and antimicrobial stewardship (4 points).Conclusions: The consensus achieved enabled the production of generic antimicrobial prescribing and stewardship competencies for all European independent prescribers, and of possible global utility. These can be used for training and can be further adapted to the needs of specific professional groups. (C) 2018 The Authors. Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases. Show less
This thesis focused on the development of creative thinking across adolescence and into adulthood. To this end, a range of creativity tasks, both with and without an fMRI scanner, and before and... Show moreThis thesis focused on the development of creative thinking across adolescence and into adulthood. To this end, a range of creativity tasks, both with and without an fMRI scanner, and before and after training paradigms has been used to ex_amine both age- and experience-related effects on creative thinking performance during functional brain develop_ment. Chapter 1 provides a theoretical background for the research described in chapters 2 to 6. In Chapter 2, developmental trajectories of creative cognition across adolescence and early adulthood were examined using a set of tasks gauging both insight and divergent thinking in the verbal and visual domain. In Chapter 3, behavioral and neural differences for creative problem solving in middle-adolescents and adults were examined. Chapter 4 examined the neural correlates of divergent thinking in adults and adolescents. Chapter 5 focused on the effectiveness of creative ideation training in adolescents and adults. In Chapter 6, the benefits of training creativity in adolescents were examined using fMRI techniques. Finally, Chapter 7 summarizes the main results of the empirical studies presented in this thesis. Here, implications of the results are discussed and suggestions for future research are presented. Show less
