Introduction: Traditional orthopaedic training has followed an apprenticeship model whereby trainees enhance their skills by operating under guidance. However the introduction of limitations on training hours and shorter training programmes mean that alternative training strategies are required.
Aims: To perform a literature review on simulation training in arthroscopy and devise a framework that structures different simulation techniques that could be used in arthroscopic training.
Methods: A systematic search of Medline, Embase, Google Scholar and the Cochrane Databases were performed. Search terms included "virtual reality OR simulator OR simulation" and "arthroscopy OR arthroscopic".
Results: 14 studies evaluating simulators in knee, shoulder and hip arthroplasty were included. The majority of the studies demonstrated construct and transference validity but only one showed concurrent validity. More studies are required to assess its potential as a training and assessment tool, skills transference between simulators and to determine the extent of skills decay from prolonged delays in training. We also devised a "ladder of arthroscopic simulation" that provides a competency-based framework to implement different simulation strategies.
Conclusion: The incorporation of simulation into an orthopaedic curriculum will depend on a coordinated approach between many bodies. But the successful integration of simulators in other areas of surgery supports a possible role for simulation in advancing orthopaedic education.
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http://dx.doi.org/10.1016/j.ijsu.2014.04.005 | DOI Listing |
Nat Mach Intell
January 2025
Engineering Laboratory, University of Cambridge, Cambridge, UK.
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time.
View Article and Find Full Text PDFJ Transl Int Med
February 2024
Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University; NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University; Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing 100191, China.
Background And Objective: Hemodynamic changes that lead to increased blood pressure represent the main drivers of organ damage in hypertension. Prolonged increases to blood pressure can lead to vascular remodeling, which also affects vascular hemodynamics during the pathogenesis of hypertension. Exercise is beneficial for relieving hypertension, however the mechanistic link between exercise training and how it influences hemodynamics in the context of hypertension is not well understood.
View Article and Find Full Text PDFAccurate forecasting of contagious illnesses has become increasingly important to public health policymaking, and better prediction could prevent the loss of millions of lives. To better prepare for future pandemics, it is essential to improve forecasting methods and capabilities. In this work, we propose a new infectious disease forecasting model based on physics-informed neural networks (PINNs), an emerging area of scientific machine learning.
View Article and Find Full Text PDFThe neural networks offer iteration capability for low-density parity-check (LDPC) decoding with superior performance at transmission. However, to cope with increasing code length and rate, the complexity of the neural network increases significantly. This is due to the large amount of feature extraction required to maintain the error correction capability.
View Article and Find Full Text PDFThis work presents a method for simulating digital lensless holographic microscopy (DLHM) holograms using a physics-based image processing approach. While DLHM has gained significant attention in biology, biomedicine, and environmental monitoring, the current modeling of DLHM holograms has been limited, hindering potential applications, including learning-based solutions and generative model training. In this study, the DLHM propagation process is decomposed into the diffraction of a complex-valued spherical wavefront and the non-homogeneous magnification of the diffracted field that encodes the sample information, which accelerates and enhances the hologram simulation.
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