Tind Technologies (Norway)
Hes-so: ArODES Open Archive (University of Applied Sciences and Arts Western Switzerland / Haute école spécialisée de Suisse occidentale / FH Westschweiz)Not a member yet
15764 research outputs found
Sort by
Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI ::part 2 : ex vivo imaging : added value and acquisition
No full textThe value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents “Part 2” of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge
Faire récit, faire outil ::pour une pragmatique de l’archive
No full textCet article revient sur le travail archivistique que j'ai réalisé avec et auprès des archives produites par le collectif de La Déviation (Marseille) entre 2019 et 2021, dans le cadre de ma thèse de doctorat (HES-SO HEAD—Genève et LaSUR EPFL) En plus de déplier la cuisine interne de ce travail archivistique qui a donné lieu à la création d'un certain nombre d'outil de visualisation des données de l'archive, j'y reviens sur les enjeux des pratiques d'écriture mémorielle dans la constitution d'une culture des précédents fondée sur l'expérience située des initiatives collectives
Cartesian and spherical multipole expansions in anisotropic media
No full textThe multipole expansion can be formulated in spherical and Cartesian coordinates. By constructing an explicit map linking both formulations in isotropic media, we discover a lack of equivalence between them in anisotropic media. In isotropic media, the Cartesian multipole tensor can be reduced to a spherical tensor containing fewer independent components. In anisotropic media, however, the loss of propagation symmetry prevents this reduction. Consequently, non-harmonic sources radiate fields that can be projected onto a finite set of Cartesian multipole moments but require potentially infinitely many spherical moments. For harmonic sources, the link between the two approaches provides a systematic way to construct the spherical multipole expansion from the Cartesian one. The lack of equivalence between both approaches results in physically significant effects wherever the field propagation includes the Laplace operator. We demonstrate this issue in an electromagnetic radiation inverse problem in anisotropic media, including an analysis of a large-anisotropy regime and an introduction to vector spherical harmonics. We show that the use of the Cartesian approach increases the efficiency and interpretability of the model. The proposed approach opens the door to a broader application of the multipole expansion in anisotropic media
An incidental finding during a brain plasticity study ::substantial telomere length shortening after COVID-19 lockdown in the older population
No full textThe detrimental effects of lockdowns have already been proven by numerous studies, mainly using psychometric measurements. Since telomere shortening is a driver of aging and aging-associated disorders, including cognitive decline, the telomere length in the older population has been investigated in the current study. Measurements were taken over a 6-month period just before and during the 6 months that included the first lockdown. The cohort of 55 persons aged 64 to 70 years was investigated in the context of a study focusing on neuroplasticity. Participants were recruited in Germany and Switzerland and characterized by psychometric measurements concerning neurocognition and neuroplasticity. Telomere lengths were measured by real-time PCR-based LTL measurement. We found an impressive and significant decline in telomere lengths in the period that included the lockdown (2.33 (± 0.1) at T1 vs. 1.35 (± 0.1) at T2), whereas it was stable in the phase before the lockdown in the same individuals (T0 was 2.25 (± 0.1 S.E.M.) vs. T1, 2.33 (± 0.1)). Correlation of the sudden decrease revealed no linkage to health issues or general physical activity but was in trend related to a decline in the WHOQOL-BREF Social Score referring to the social interaction of the study participants. Our data support, at a biological level, the results of clinical and psychosocial studies showing the detrimental effects of lockdowns
Garde la tête froide ! ::jeu éducatif pour comprendre le rôle d’une bonne régulation des émotions sur le fonctionnement du cerveau chez les 20-25 ans
No full textLe présent travail s’inscrit dans le cadre du Certificate of Advanced Studies (CAS) en neurosciencesde l’éducation et explore l’impact de la régulation émotionnelle sur le raisonnement clinique des étudiant·e·s sages-femmes (ESF) à la Haute École de Santé Vaud (HESAV). Partant du constat que ces étudiant·e·s rencontrent des difficultés à mobiliser efficacement leurs fonctions exécutives dans des situations de stress, notamment en stage clinique, un jeu éducatif – HOT SPOT Garde la tête froide – a été conçu et testé. Ce jeu de plateau vise à sensibiliser les ESF aux mécanismes cérébraux impliqués dans la gestion du stress et à leur fournir des stratégies concrètes d’autorégulation émotionnelle. L’étude repose sur une approche combinant neurosciences cognitives et pédagogie active. Les concepts clés incluent le rôle des fonctions exécutives, la neuroplasticité et l’importance du cortex préfrontal dans la prise de décision sous pression. Les résultats obtenus à partir de questionnaires administrés avant et après l’expérimentation du jeu montrent une amélioration notable de la compréhension et de la gestion des émotions chez les participant·e·s. De plus, les ESF ont jugé le jeu utile non seulement pour leur formation, mais aussi pour leur quotidien en général. Les conclusions soulignent l’efficacité des approches ludiques dans l’apprentissage et suggèrent l’intégration d’outils similaires dans la formation des professionnel·le·s de santé. Ce travail ouvre également la voie à de futures recherches sur l’impact de la neuroéducation dans l’amélioration des compétences cliniques et de la résilience émotionnelle
Structured radiology report text analysis using natural language processing for automatic billing
No full textPurpose: The aim of this study was to develop an algorithm for automated quality control of structured radiology reports and to automatically obtain the correct invoicing codes for the performed exam. Ultrasound (US) exams of the abdomen were selected as use case, including Doppler exams. Method: To build a correct algorithm for automatic billing, the billing tree for the Ultrasound exams was studied. In Switzerland, TARMED, is the tariff structure used for billing outpatient medical services. The 4600 services listed in TARMED are divided into chapters that group together all services with a well-defined common characteristic. For example, Chapter 39 covers all medical imaging services. These chapters are further subdivided into subchapters for greater precision. Using this information a modular Natural Language Processing algorithm based on the Natural Language Toolkit (NLTK) library was developed. A second NLP algorithm based on SPACY was also developed, with the objective of a double validation of the first developed NLP algorithm. To train and test the algorithm a dataset of 170 exams corresponding to US abdominal examinations along with their radiology report were extracted from our RIS. The results of the algorithm were validated by an experienced technologists which identified possible discrepancy between the algorithm results and the correct billing. This check was carried out on a batch of data containing 95 samples. A confusion matrix was used to analyze the results. Results: In all 95 data samples, the NLTK algorithm was able to detect the billing codes correctly 100% of the time. In all our 95 data samples, the Spacy algorithm was able to detect the billing codes correctly in 86.3% of cases. This algorithm tends to overestimate the type of abdominal examination present in the report. Indeed, the 13 cases in which the algorithm made an error were cases where it detected a full abdominal ultrasound when the examination was a simple lower or upper abdomen. Conclusion: The NLTK model provides reliable and efficient estimation of billing codes for abdominal ultrasound, facilitating the task of the technologies who saves time and avoids possible human errors
From chains to chains ::blockchain innovations in combating modern slavery and enhancing reporting mechanisms
No full textAutomatic cranial defect reconstruction with self-supervised deep deformable masked autoencoders
No full textThousands of people suffer from cranial injuries every year. They require personalized implants that need to be designed and manufactured before the reconstruction surgery. The manual design is expensive and time-consuming leading to searching for algorithms whose goal is to automatize the process. The problem can be formulated as volumetric shape completion and solved by deep neural networks dedicated to supervised image segmentation. However, such an approach requires annotating the ground-truth defects which is costly and time-consuming. Usually, the process is replaced with synthetic defect generation. However, even the synthetic ground-truth generation is time-consuming and limits the data heterogeneity, thus the deep models’ generalizability. In our work, we propose an alternative and simple approach to use a self-supervised masked autoencoder to solve the problem. This approach by design increases the heterogeneity of the training set and can be seen as a form of data augmentation. We compare the proposed method with several state-of-the-art deep neural networks and show both the quantitative and qualitative improvement on the SkullBreak and SkullFix datasets. The proposed method can be used to efficiently reconstruct the cranial defects in real time
Overview of ImageCLEFmedical 2024 – caption prediction and concept detection
No full textThe ImageCLEFmedical 2024 Caption task on caption prediction and concept detection follows similar challenges held from 2017–2023. The goal is to extract Unified Medical Language System (UMLS) concept annotations and/or define captions from image data. Predictions are compared to original image captions. Images for both tasks are part of the Radiology Objects in COntext version 2 (ROCOv2) dataset. For concept detection, multi-label predictions are compared against UMLS terms extracted from the original captions with additional manually curated concepts via the F1-score. For caption prediction, the semantic similarity of the predictions to the original captions is evaluated using the BERTScore. The task attracted strong participation with 50 registered teams,14 teams submitted 82 graded runs for the two subtasks. Participants mainly used multi-label classification systems for the concept detection subtask, the winning team DBS-HHU utilized an ensemble of four different Convolutional Neural Networks (CNNs). For the caption prediction subtask, most teams used encoder-decoder frameworks with various backbones, including transformer-based decoders and Long Short-Term Memories (LSTMs), with the winning team PCLmed using medical vision-language foundation models (Med-VLFMs) by combining general and specialist vision models