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dc.contributor.authorFidalgo Herrera, Alberto Javieres-ES
dc.contributor.authorMartínez Beltrán, María Jesúses-ES
dc.contributor.authorde la Torre Montero, Julio Césares-ES
dc.contributor.authorMoreno Ruiz, Jose Andréses-ES
dc.contributor.authorBarton, Gabores-ES
dc.date.accessioned2021-01-10T16:29:44Z
dc.date.available2021-01-10T16:29:44Z
dc.date.issued17/12/2020es_ES
dc.identifier.issn1932-6203es_ES
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0243816es_ES
dc.identifier.urihttp://hdl.handle.net/11531/53655
dc.descriptionArtículos en revistases_ES
dc.description.abstract.es-ES
dc.description.abstractThe active cervical range of motion (aROM) is assessed by clinicians to inform their decision-making. Even with the ability of neck motion to discriminate injured from non-injured subjects, the mechanisms to explain recovery or persistence of WAD remain unclear. There are few studies of ROM examinations with precision tools using kinematics as predictive factors of recovery rate. The present paper will evaluate the performance of an artificial neural network (ANN) using kinematic variables to predict the overall change of aROM after a period of rehabilitation in WAD patients. To achieve this goal the neck kinematics of a cohort of 1082 WAD patients (55.1% females), with mean age 37.68 (SD 12.88) years old, from across Spain were used. Prediction variables were the kinematics recorded by the EBI® 5 in routine biomechanical assessments of these patients. These include normalized ROM, speed to peak and ROM coefficient of variation. The improvement of aROM was represented by the Neck Functional Holistic Analysis Score (NFHAS). A supervised multi-layer feed-forward ANN was created to predict the change in NFHAS. The selected architecture of the ANN showed a mean squared error of 308.07-272.75 confidence interval for a 95% in the Monte Carlo cross validation. The performance of the ANN was tested with a subsample of patients not used in the training. This comparison resulted in a medium correlation with R = 0.5. The trained neural network to predict the expected difference in NFHAS between baseline and follow up showed modest results. While the overall performance is moderately correlated, the error of this prediction is still too large to use the method in clinical practice. The addition of other clinically relevant factors could further improve prediction performance.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoes-ESes_ES
dc.rightsCreative Commons Reconocimiento-NoComercial-SinObraDerivada Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/es_ES
dc.sourceRevista: PLoS One, Periodo: 1, Volumen: 15, Número: 12, Página inicial: e0243816, Página final: e0243816es_ES
dc.subject.otherBienestar, salud y sociedades_ES
dc.titleArtificial intelligence prediction of the effect of rehabilitation in whiplash-associated disorderes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywordsLatigazo cervical, rehabilitación, inteligencia artificiales-ES
dc.keywordsArtificial intelligence, prediction, rehabilitation, whiplashen-GB


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