Computer assisted simulation is an important teaching modality in the preclinical training of students. In order to maximize the potential of this learning tool, the University of Tennessee's College of Dentistry has successfully incorporated DentSim technology into the restorative curriculum and has recently acquired the technology to make image guided implantology available to students, residents and faculty. This article describes the university's history and experience with simulation as a learning tool. The purpose of this article is to provide information to other educational institutions on the use of virtual reality simulation in the classroom.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Fracture resistance of analog and CAD-CAM long-span fixed provisional restorations: An in vitro experimental study.
J Prosthodont
January 2025
The Office of Assistant Dean for Research, School of Dental Medicine, Medical Sciences Campus, University of Puerto Rico, San Juan, Puerto Rico.
Purpose: This study aimed to evaluate and compare the fracture resistance of long-span fixed provisional restorations fabricated using milling, three-dimensional (3D) printing, and conventional methods.
Materials And Methods: Sixty specimens were prepared, divided into four groups of 15 each, corresponding to four fabrication methods: computer-aided design and computer-aided manufacturing (CAD-CAM) milled provisional resins, 3D-printed provisional resins, 3D-printed permanent resins, and conventional bis-acryl restorations reinforced with wire. The specimens underwent a three-point bending test using a universal testing machine to measure fracture resistance, quantified as maximum force (in Newtons).
Refining MR-guided thermal ablation for HCC within the Milan criteria: a decade of clinical outcomes and predictive modeling at a single institution.
BMC Cancer
January 2025
Department of Interventional Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, China.
Background: The appropriateness of ablation for liver cancer patients meeting the Milan criteria remains controversial.
Purpose: This study aims to evaluate the long-term outcomes of MR-guided thermal ablation for HCC patients meeting the Milan criteria and develop a nomogram for predicting survival rates.
Methods: A retrospective analysis was conducted from January 2009 to December 2021 at a single institution.
Investigating time-independent and time-dependent diffusion phenomena using steady-state diffusion MRI.
Sci Rep
January 2025
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Diffusion MRI is a leading method to non-invasively characterise brain tissue microstructure across multiple domains and scales. Diffusion-weighted steady-state free precession (DW-SSFP) is an established imaging sequence for post-mortem MRI, addressing the challenging imaging environment of fixed tissue with short T and low diffusivities. However, a current limitation of DW-SSFP is signal interpretation: it is not clear what diffusion 'regime' the sequence probes and therefore its potential to characterise tissue microstructure.
View Article and Find Full Text PDFDilated SE-DenseNet for brain tumor MRI classification.
Sci Rep
January 2025
Department of Applied Mathematics, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks' attention mechanisms. This novel approach aims to improve upon state-of-the-art methods of tumor identification. Our model, trained and evaluated on a comprehensive Kaggle brain tumor dataset, demonstrated superior performance over established convolution-based and transformer-based models: ResNet-101, VGG-19, original DenseNet-121, MobileNet-V2, ViT-L/16, and Swin-B across key metrics: F1-score, accuracy, precision, and recall.
View Article and Find Full Text PDFDevelopment of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach.
Sci Rep
January 2025
Amal Jyothi College of Engineering (Autonomous), Kanjirappally, Kerala, India.
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!