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https://www.selleckchem.com/products/pi4kiiibeta-in-10.html This study outlines the first investigation of application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality (VR) brain tumor resection task. Tumor resection task participants included 23 neurosurgeons and senior neurosurgery residents as the "skilled" group and 92 junior neurosurgery residents and medical students as the "novice" group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue. Originally, 150 features were extracted followed by statistical and forward feature selection. The selected features were provided to 4 classifiers, namely, K-Nearest Neighbors, Parzen Window, Support Vector Machine, and Fuzzy K-Nearest Neighbors. Sets of 5 to 30 selected features were provided to the classifiers. A working point of 15 premium features resulted in accuracy values as high as 90% using the Supprt Vector Machine. The obtained results highlight the potentials of machine learning, applied to VR simulation data, to help realign the traditional apprenticeship educational paradigm to a more objective model, based on proven performance standards. Graphical abstract Using several scenarios of virtual reality neurosurgical tumor resection together with machine learning classifiers to distinguish skill level.Despite all the efforts to optimize the meniscus prosthesis system (geometry, material, and fixation type), the success of the prosthesis in clinical practice will depend on surgical factors such as intra-operative positioning of the prosthesis. In this study, the aim was therefore to assess the implications of positional changes of the medial meniscus prosthesis for knee biomechanics. A detailed validated finite element (FE) model of human intact and meniscal implanted knees was developed based on a series of in vitro experiments. Different non-anatomical prosthesis positions were applied in the FE model

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