Fiber tracking is a powerful technique that provides insight into the brain's white matter structure. Despite its potential, the inherent uncertainties limit its widespread clinical use. These uncertainties potentially hamper the clinical decisions neurosurgeons have to make before, during, and after the surgery. Many techniques have been developed to visualize uncertainties, however, there is limited evidence to suggest whether these uncertainty visualization influences neurosurgical decision-making. In this paper, we evaluate the hypothesis that uncertainty visualization in fiber tracking influences neurosurgeon's decisions and the confidence in their decisions. For this purpose, we designed a user study through an online interactive questionnaire and evaluate the influence of uncertainty visualization in neurosurgical decision-making. The results of this study emphasize the importance of uncertainty visualization in clinical decision making by highlighting the influence of different interval of uncertainty visualization in critical clinical decisions.
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http://dx.doi.org/10.1109/MCG.2024.3462926 | DOI Listing |
Clin Neuroradiol
January 2025
Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany.
Introduction: Ventriculoperitoneal shunts (VPS) are an essential part of the treatment of hydrocephalus, with numerous valve models available with different ways of indicating pressure levels. The model types often need to be identified on X‑rays to assess pressure levels using a matching template. Artificial intelligence (AI), in particular deep learning, is ideally suited to automate repetitive tasks such as identifying different VPS valve models.
View Article and Find Full Text PDFPLoS One
January 2025
Orthopedics Department, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Objective: The objective of this systematic review and meta-analysis is to clarify the rehabilitation efficacy of virtual reality (VR) balance training after anterior cruciate ligament reconstruction (ACLR).
Methods: This meta-analysis was registered in PROSPERO with the registration number CRD42024520383. The electronic databases PubMed, Web of Science, Cochrane Library, MEDLINE, Embase, China National Knowledge Infrastructure, Chinese Biomedical Literature, China Science and Technology Journal Database, and Wanfang Digital Periodical database were systematically searched to identify eligible studies from their inception up to January 2024.
Environ Microbiol
January 2025
Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA.
Ecological assembly-the process of ecological community formation through species introductions-has recently seen exciting theoretical advancements across dynamical, informational, and probabilistic approaches. However, these theories often remain inaccessible to non-theoreticians, and they lack a unifying lens. Here, I introduce the assembly graph as an integrative tool to connect these emerging theories.
View Article and Find Full Text PDFJ Cogn
January 2025
Department of Psychology, Humboldt-Universität zu Berlin, Berlin, DE.
Visual working memory and verbal storage are often investigated independently of one another. However, a growing body of evidence suggests that naming visual stimuli can provide an advantage in performance during visual working memory tasks. On the other hand, there is also evidence that labeling could lead to biases in recall.
View Article and Find Full Text PDFMed Image Anal
January 2025
School of Computer Science and Technology, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China; National Key Laboratory of Smart Farm Technologies and Systems, Harbin, 150001, China. Electronic address:
Despite that supervised learning has demonstrated impressive accuracy in medical image segmentation, its reliance on large labeled datasets poses a challenge due to the effort and expertise required for data acquisition. Semi-supervised learning has emerged as a potential solution. However, it tends to yield satisfactory segmentation performance in the central region of the foreground, but struggles in the edge region.
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