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3Poster KIM voor de ECR is nu online te zien via EPOS: https://epos.myesr.org/poster/esr/ecr2022/C-16092 posternummer: C-16092, ECR 2022 Purpose Artificial Intelligence (AI) has developed at high speed the last few years and will substantially change various disciplines (1,2). These changes are also noticeable in the field of radiology, nuclear medicine and radiotherapy. However, the focus of attention has mainly been on the radiologist profession, whereas the role of the radiographer has been largely ignored (3). As long as AI for radiology was focused on image recognition and diagnosis, the little attention for the radiographer might be justifiable. But with AI becoming more and more a part of the workflow management, treatment planning and image reconstruction for example, the work of the radiographer will change. However, their training (courses Medical Imaging and Radiotherapeutic Techniques) hardly contain any AI education. Radiographers in the Netherlands are therefore not prepared for changes that will come with the introduction of AI into everyday work.
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Abstract gepubliceerd in Elsevier: Introduction: Recent research has identified the issue of ‘dose creep’ in diagnostic radiography and claims it is due to the introduction of CR and DR technology. More recently radiographers have reported that they do not regularly manipulate exposure factors for different sized patients and rely on pre-set exposures. The aim of the study was to identify any variation in knowledge and radiographic practice across Europe when imaging the chest, abdomen and pelvis using digital imaging. Methods: A random selection of 50% of educational institutes (n ¼ 17) which were affiliated members of the European Federation of Radiographer Societies (EFRS) were contacted via their contact details supplied on the EFRS website. Each of these institutes identified appropriate radiographic staff in their clinical network to complete an online survey via SurveyMonkey. Data was collected on exposures used for 3 common x-ray examinations using CR/DR, range of equipment in use, staff educational training and awareness of DRL. Descriptive statistics were performed with the aid of Excel and SPSS version 21. Results: A response rate of 70% was achieved from the affiliated educational members of EFRS and a rate of 55% from the individual hospitals in 12 countries across Europe. Variation was identified in practice when imaging the chest, abdomen and pelvis using both CR and DR digital systems. There is wide variation in radiographer training/education across countries.
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Artificial Intelligence (AI) offers promising opportunities for innovating the field of medical imaging, yet its practical implementation remains limited. Two important reasons for this are strict legislation and the unavailability of data, partly due to patient privacy. Synthetic data (SD) could offer a solution to the latter. Synthetic data is a machine-learning (ML) technique that learns the distributions and correlations of a real dataset and generates a synthetic dataset with the same characteristics, without containing real data. Synthetic data could facilitate multi-centre collaborations enabling the training of ML-models with data that are now limited. Our aim was to investigate the effects of replacing real data with synthetic data on ML-model performance using SD-generators for the prediction of metastases on 18F-PSMA-1007 PET/CT using a tabular dataset.
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Introduction: The Netherlands does not have a national guideline for performing radiographic examinations on pregnant patients. Radiographic examination is a generic term for all examinations performed using ionizing radiation, including but not limited to radiographs, fluoroscopy and computed tomography. A pilot study amongst radiographers (Medical Radiation Technologists (MRTs)) showed that standardized practice of radiographic examinations on pregnant women is not evident between Radiology departments and that there is a need for a national guideline as the varying practice methods may lead to confusion and uncertainty amongst both patients and MRTs. Methods: Focus groups consisting of MRTs from several Radiology departments within the Netherlands were used to map ideas and requirements as to what should be included in the national guideline. Nine focus group sessions were organized with a total of 52 participants. Using a previous review (Wit, Fleur; Vroonland, Colinda; Bijwaard H. Pre-natal X-ray exposure and the risk of developing paediatric cancer; a systematic review of risk factors and a comparison of international guidelines. Health Physics 2021; 121 (3):225e233), the following key points were chosen as discussion topics for the focus group sessions: dose reduction, confirming pregnancy and risk communication. Results: Results showed that the participating MRTs did not agree on the use of lead aprons. That the national guideline should include standardized methods to adjust parameters to decrease radiation dose. Focus group participants find it difficult to ask a patient's pregnancy status, especially when dealing with relatively young and old (er) patients. When communicating the level of risk associated with a radiographic examination the participating MRTs would like to be able to use examples and comparisons, preferably by means of a multilingual website. Conclusion: A national guideline must include information on justification, available alternatives, dose reductions methods and confirmation of pregnancy requirements when fetal dose is a significant risk. Implications for practice: A national guideline ensures standardized practice can be implemented in Radiology departments, increasing clarity of the issues for both patients and MRTs.
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Objective: In myocardial perfusion single-photon emission computed tomography (SPECT), abdominal activity often interferes with the evaluation of perfusion in the inferior wall, especially after pharmacological stress. In this randomized study, we examined the effect of carbonated water intake versus still water intake on the quality of images obtained during myocardial perfusion images (MPI) studies. Methods: A total of 467 MIBI studies were randomized into a carbonated water group and a water group. The presence of intestinal activity adjacent to the inferior wall was evaluated by two observers. Furthermore, a semiquantitative analysis was performed in the adenosine subgroup,using a count ratio of the inferior myocardial wall and adjacent abdominal activity. Results: The need for repeated SPECT in the adenosine studies was 5.3 % in the carbonated water group versus 19.4 % in the still water group (p = 0.019). The inferior wall-to-abdomen count ratio was significantly higher in the carbonated water group compared to the still water group (2.11 ± 1.00 vs. 1.72 ± 0.73, p\0.001). The effect of carbonated water during rest and after exercise was not significant. Conclusions: This randomized study showed that carbonated water significantly reduced the interference of extra-cardiac activity in adenosine SPECT MPI. Keywords: Extra-cardiac radioactivity, Myocardial SPECT, Image quality enhancement, Carbonated water
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Background
In 2015, Amsterdam became part of the WHO Age Friendly City community, thereby accepting the responsibility to work towards a more age friendly Amsterdam. To study senior citizens’ needs and wishes concerning the age friendliness of their neighbourhood, the municipality asked the Amsterdam University of Applied Science to set up two pilot projects in two neighbourhoods. Aim was to 1) gain insight in seniors’ views and wishes regarding an age friendly city, and 2) reflect on the experiences with working with senior co-researchers.
Methodology
The study followed a Participatory Action Research approach with research teams consisting of seniors as co-researchers and professional researchers. We chose two neighbourhoods with distinct characteristics: the Indische Buurt which is centrally located, vibrant, multicultural, and strongly gentrifying, and Buitenveldert, a suburban and spacious neighbourhood, with less facilities and a dominance of well-to-do senior citizens. In both areas, we recruited senior co-researchers to form the research teams. They generally lived in, or close to, the pilot neighbourhood, and varied in age and ethnical background. The aim was to put the co-researchers in the lead during the entire research process. However, it differed between the neighbourhoods which type of researcher was in the lead. As a team, they formulated the main research question, constructed a topic list for interviews with older citizens, convened the interviews, analysed the data, wrote the report, and presented the results. During the entire process, they were supported by professional researchers.
Both research teams interviewed 40 senior citizens, who were recruited through the co-researchers’ networks, professional care organisations, neighbourhood communities, and local media. We intended to gather a sample representative for the neighbourhood population. In the Indische Buurt, this proved to be difficult, since the relatively large Turkish and Moroccan communities were difficult to get into contact with, and it was hard to find co-researchers from those communities who could have provided a way in.
Process and outcomes
We will share some of the results, but we will mainly reflect on the research process.
Process
Regarding the process, we found some differences between the two neighbourhoods. In the Indische Buurt, it took much effort to find co-researchers, since the seniors we encountered said to be too busy with other neighbourhood activities. We did recruit a small group of four co-researchers of different ethnical background, but sadly lacking Turkish and Moroccan seniors. They started with a very limited research experience and experienced ownership, which greatly increased during the process. At the finalisation of the project, the group ceased to be, but the outcomes were followed up by existing groups and organisations in the neighbourhood.
In Buitenveldert, a large group of co-researchers was recruited in no-time, bearing more resemblance to an action group than a research group. They were generally highly educated and some already had research experience. The group proved to be pro-active, had a strong feeling of ownership, and worked in constant collaboration with the ‘professional’ researchers, respecting each other’s knowledge and skills. At the finalisation of the project, the group remained active as partner of the local government.
Outcomes
Concerning the content of the outcomes, we found some expected differences and unexpected similarities. For instance, we expected to find different outcomes concerning housing and facilities between the neighbourhoods. Indeed, in Buitenveldert, housing was already age friendly whereas facilities were scarce and geographically far apart. Yet, in the Indische Buurt, housing was poorly equipped for physically impaired seniors, but facilities were abundant and close by.
We also found that, in both neighbourhoods, senior citizens were reluctant to share their limitations and ask for support, despite differences in neighbourhood, ethnicity, age etc. Of course, this can be expected of seniors from the ‘silent generation’. However, they seemingly shared these emotions more easily with their peers than with professional researchers.
Conclusion
The social-cultural context of the neighbourhood impacts the research process. Overall, co-research appears to be a fruitful method to involve senior citizens in decisions concerning the improvement of their neighbourhood.
Aims and content of the workshop
We aim to:
• present our reflections on the participative process of working with senior co-researchers in Amsterdam
• exchange and discuss with the participants of the workshop the lessons learned on how to facilitate citizens’ participation in the community
• discuss similar and future projects and possibilities for collaboration among the participants of the workshop
Content of the workshop
• Presentation
• Exchange and discussion in small groups
• Plenary discussion on possible collaboration projects aiming to enhance citizens’ participation in the community
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Our study shows a steady increase in dementia- and DHT-related publications, particularly in areas such as mobile health, virtual reality, artificial intelligence, and sensor-based technologies interventions. This increase underscores the importance of systematic approaches and interdisciplinary collaborations, while identifying knowledge gaps, especially in lower-income regions. It is crucial that researchers worldwide adhere to evidence-based medicine principles to avoid duplication of efforts. This analysis offers a valuable foundation for policy makers and academics, emphasizing the need for an international collaborative task force to address knowledge gaps and advance dementia care globally.
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Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.
Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).
Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.
Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.
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