The heart is a three-dimensional (3-D) object and, with the help of 3-D echocardiography (3-DE), it can be shown in a realistic fashion. This capability decreases variability in the interpretation of complex pathology among investigators. Therefore, it is likely that the method will become the standard echocardiography examination in the future. The availability of volumetric data sets allows retrieval of an infinite number of cardiac cross-sections. This results in more accurate and reproducible measurements of valve areas, cardiac mass and cavity volumes by obviating geometric assumptions. Typical 3-DE parameters, such as ejection fraction, flow jets, myocardial perfusion and LV wall curvature, may become important diagnostic parameters based on 3-DE. However, the freedom of an infinite number of cross-sections of the heart can result in an often-encountered problem of being "lost in space" when an observer works on a 3-DE image data set. Virtual reality computing techniques in the form of a virtual heart model can be useful by providing spatial "cardiac" information. With the recent introduction of relatively low cost portable echo devices, it is envisaged that use of diagnostic ultrasound (US) will be further boosted. This, in turn, will require further teaching facilities. Coupling of a cardiac model with true 3-D echo data in a virtual reality setting may be the answer.
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http://dx.doi.org/10.1016/s0301-5629(00)00256-8 | DOI Listing |
J Neural Eng
January 2025
The University of Chicago Biological Sciences Division, Department of Organismal Biology & Anatomy, Chicago, Illinois, 60637, UNITED STATES.
Objective: As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device. Approach: Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments.
View Article and Find Full Text PDFInteract J Med Res
January 2025
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain.
Objective: This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century.
PLoS One
January 2025
Department of Nutrition, College of Human Health and Development, The Pennsylvania State University, University Park, PA, United States of America.
Objective: To understand the impact of fidelity and perceived realism on virtual reality food choices, and task motivation, engagement, and interest.
Design: Randomized controlled trial.
Setting: Online.
J Exp Biol
January 2025
Centre de Recherches sur la Cognition Animale, CNRS, Université Paul Sabatier, Toulouse 31062 cedex 09, France.
Solitary foraging insects like desert ants rely heavily on vision for navigation. While ants can learn visual scenes, it is unclear what cues they use to decide if a scene is worth exploring at the first place. To investigate this, we recorded the motor behavior of Cataglyphis velox ants navigating in a virtual reality set-up (VR) and measured their lateral oscillations in response to various unfamiliar visual scenes under both closed-loop and open-loop conditions.
View Article and Find Full Text PDFFront Behav Neurosci
January 2025
Economic, Psychological and Communication Sciences Department, Niccolò Cusano University, Rome, Italy.
This mini-review examines the available papers about virtual reality (VR) as a tool for the diagnosis or therapy of neurodevelopmental disorders, focusing on Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and Specific Learning Disorders (SLD). Through a search on literature, we selected 62 studies published between 1998 and 2024. After exclusion criteria, our synoptic table includes 32 studies on ADHD (17 were on diagnostic evaluation and 15 were on therapeutic interventions), 2 on pure ASD, and 2 on pure SLD.
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