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Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

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Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

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dc.contributor.author Villamor, E. es_ES
dc.contributor.author Monserrat Aranda, Carlos es_ES
dc.contributor.author Del Río, L. es_ES
dc.contributor.author Romero-Martín, J.A. es_ES
dc.contributor.author Rupérez Moreno, María José es_ES
dc.date.accessioned 2021-05-13T03:32:02Z
dc.date.available 2021-05-13T03:32:02Z
dc.date.issued 2020-09 es_ES
dc.identifier.issn 0169-2607 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166263
dc.description.abstract [EN] A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 +/- 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow. es_ES
dc.description.sponsorship This study was partially funded by two grants Catedra UPVFundacion Quaes, obtained by Eduardo Villamor Medina and Antonio Cutillas Pardines, and one FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor Medina. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computer Methods and Programs in Biomedicine es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Hip fracture es_ES
dc.subject Clinical Osteoporosis es_ES
dc.subject Finite element es_ES
dc.subject Machine learning es_ES
dc.subject.classification INGENIERIA MECANICA es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cmpb.2020.105484 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//SP20170111/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Mecánica y de Materiales - Departament d'Enginyeria Mecànica i de Materials es_ES
dc.description.bibliographicCitation Villamor, E.; Monserrat Aranda, C.; Del Río, L.; Romero-Martín, J.; Rupérez Moreno, MJ. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine. 193:1-11. https://doi.org/10.1016/j.cmpb.2020.105484 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.cmpb.2020.105484 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 11 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 193 es_ES
dc.identifier.pmid 32278980 es_ES
dc.relation.pasarela S\407379 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Cátedra Fundación QUAES, Universitat Politècnica de València es_ES
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