000101530 001__ 101530
000101530 005__ 20231127095531.0
000101530 0247_ $$2doi$$a10.1080/10255842.2020.1757661
000101530 0248_ $$2sideral$$a117917
000101530 037__ $$aART-2020-117917
000101530 041__ $$aeng
000101530 100__ $$aAlastruey-López, D.
000101530 245__ $$aUsing artificial neural networks to predict impingement and dislocation in total hip arthroplasty
000101530 260__ $$c2020
000101530 5060_ $$aAccess copy available to the general public$$fUnrestricted
000101530 5203_ $$aDislocation 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.
000101530 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/DPI2014-53401-C2-1-R$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-84780-C2-1-R
000101530 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000101530 590__ $$a1.763$$b2020
000101530 591__ $$aENGINEERING, BIOMEDICAL$$b72 / 89 = 0.809$$c2020$$dQ4$$eT3
000101530 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b86 / 111 = 0.775$$c2020$$dQ4$$eT3
000101530 592__ $$a0.353$$b2020
000101530 593__ $$aBioengineering$$c2020$$dQ3
000101530 593__ $$aBiomedical Engineering$$c2020$$dQ3
000101530 593__ $$aMedicine (miscellaneous)$$c2020$$dQ3
000101530 593__ $$aHuman-Computer Interaction$$c2020$$dQ3
000101530 593__ $$aComputer Science Applications$$c2020$$dQ3
000101530 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000101530 700__ $$aEzquerra, L.$$uUniversidad de Zaragoza
000101530 700__ $$0(orcid)0000-0002-4502-460X$$aSeral, B.$$uUniversidad de Zaragoza
000101530 700__ $$0(orcid)0000-0002-2901-4188$$aPérez, M.A.$$uUniversidad de Zaragoza
000101530 7102_ $$11013$$2830$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Traumatología y Ortopedia
000101530 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000101530 773__ $$g23, 10 (2020), 649-657$$pComput. methods biomech. biomed. eng.$$tComputer Methods in Biomechanics and Biomedical Engineering$$x1025-5842
000101530 8564_ $$s353588$$uhttps://zaguan.unizar.es/record/101530/files/texto_completo.pdf$$yPostprint
000101530 8564_ $$s868148$$uhttps://zaguan.unizar.es/record/101530/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000101530 909CO $$ooai:zaguan.unizar.es:101530$$particulos$$pdriver
000101530 951__ $$a2023-11-27-09:47:02
000101530 980__ $$aARTICLE