101530 20231127095531.0 doi 10.1080/10255842.2020.1757661 sideral 117917 ART-2020-117917 eng Alastruey-López, D. Using artificial neural networks to predict impingement and dislocation in total hip arthroplasty 2020 Access copy available to the general public Unrestricted Dislocation after total hip arthroplasty (THA) remains a major issue and an important post-surgical complication. Impingement and subsequent dislocation are influenced by the design (head size) and position (anteversion and abduction angles) of the acetabulum and different movements of the patient, with external extension and internal flexion the most critical movements. The aim of this study is to develop a computational tool based on a three-dimensional (3D) parametric finite element (FE) model and an artificial neural network (ANN) to assist clinicians in identifying the optimal prosthesis design and position of the acetabular cup to reduce the probability of impingement and dislocation. A 3D parametric model of a THA was used. The model parameters were the femoral head size and the acetabulum abduction and anteversion angles. Simulations run with this parametric model were used to train an ANN, which predicts the range of movement (ROM) before impingement and dislocation. This study recreates different configurations and obtains absolute errors lower than 5.5° between the ROM obtained from the FE simulations and the ANN predictions. The ROM is also predicted for patients who had already suffered dislocation after THA, and the computational predictions confirm the patient’s dislocations. Summarising, the combination of a 3D parametric FE model of a THA and an ANN is a useful computational tool to predict the ROM allowed for different designs of prosthesis heads. info:eu-repo/grantAgreement/ES/MINECO/DPI2014-53401-C2-1-R info:eu-repo/grantAgreement/ES/MINECO/DPI2017-84780-C2-1-R info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 1.763 2020 ENGINEERING, BIOMEDICAL 72 / 89 = 0.809 2020 Q4 T3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 86 / 111 = 0.775 2020 Q4 T3 0.353 2020 Bioengineering 2020 Q3 Biomedical Engineering 2020 Q3 Medicine (miscellaneous) 2020 Q3 Human-Computer Interaction 2020 Q3 Computer Science Applications 2020 Q3 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Ezquerra, L. Universidad de Zaragoza Seral, B. Universidad de Zaragoza (orcid)0000-0002-4502-460X Pérez, M.A. Universidad de Zaragoza (orcid)0000-0002-2901-4188 1013 830 Universidad de Zaragoza Dpto. Cirugía Área Traumatología y Ortopedia 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 23, 10 (2020), 649-657 Comput. methods biomech. biomed. eng. Computer Methods in Biomechanics and Biomedical Engineering 1025-5842 353588 http://zaguan.unizar.es/record/101530/files/texto_completo.pdf Postprint 868148 http://zaguan.unizar.es/record/101530/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:101530 articulos driver 2023-11-27-09:47:02 ARTICLE