The Flow Diverter is a self-expandable braided stent that has helped improve the effectiveness of cerebral aneurysm treatment during the last decade. The Flow Diverter's efficiency heavily relies on proper decision-making during the pre-operative phase, which is currently based on static measurements that fail to account for vessel or tissue deformation. In the context of providing realistic measurements, a biomechanical computational method is designed to aid physicians in predicting patient-specific treatment outcomes. The method integrates virtual and analytical treatment models, validated against experimental mechanical tests, and two patient treatment outcomes. In the case of both patients, deployed stent length was one of the validated result parameters, which displayed an error inferior to 1.5% for the virtual and analytical models. These results indicated both models' accuracy. However, the analytical model provided more accurate results with a 0.3% error while requiring a lower computational cost for length prediction. This computational method can offer designing and testing platforms for predicting possible intervention-related complications, patient-specific medical device designs, and pre-operative planning to automate interventional procedures.
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http://dx.doi.org/10.1016/j.medengphy.2024.104145 | DOI Listing |
Biomed Tech (Berl)
December 2024
Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany.
Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).
View Article and Find Full Text PDFSci Rep
December 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG's and improve the robustness of the system under varying load and generation conditions.
View Article and Find Full Text PDFNurs Rep
December 2024
School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA 30144, USA.
Background/objectives: The projected increase from 58 million older adults in 2022 to 82 million by 2050 in the United States highlights the urgency of preparing nursing students to care for this aging population. However, studies reveal negative attitudes among nursing students toward older adults. A three-phased educational intervention that included an artificial intelligence (AI)-driven virtual simulation was implemented to address this.
View Article and Find Full Text PDFJ Imaging
November 2024
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.
In recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow parameters from videos captured during snowfall conditions has become imperative for numerous future applications. This paper proposes a new analytical framework designed to extract traffic flow parameters from traffic flow videos recorded under snowfall conditions.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.
Enabling a robot to learn skills from a human and adapt to different task scenarios will enable the use of robots in manufacturing to improve efficiency. Movement Primitives (MPs) are prominent tools for encoding skills. This paper investigates how to learn MPs from a small number of human demonstrations and adapt to different task constraints, including waypoints, joint limits, virtual walls, and obstacles.
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