La figura de rey como instrumento de clasificación de deterioro cognitivo con el apoyo de inteligencia artificial

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dc.contributor.advisorSalazar Montes, Ana María
dc.contributor.authorCastro Rodríguez, Juan Gabriel
dc.date.accessioned2024-11-18T20:23:46Z
dc.date.available2024-11-18T20:23:46Z
dc.date.issued2024-10
dc.description.abstractLa Figura Compleja de Rey (FCR) es ampliamente utilizada para la evaluación y diagnóstico del deterioro cognitivo. En los últimos años han surgido sistemas automatizados basados en Redes Neuronales Convolucionales (RNC), que buscan evaluar la FCR como herramienta de apoyo en el diagnóstico de Deterioro Cognitivo Leve (DCL) y demencia, observándose resultados positivos en la precisión. Por ello, el objetivo de este estudio fue validar un modelo de RNC que usa la FCR en la fase de copia, para la clasificación de DCL y demencia. Se obtuvieron un total de 1.593imágenes, de las cuales 547 fueron de pacientes con normalidad, 655 con DCL y 379 con demencia. El modelo fue desarrollado con Deep Learning (DL), con la inclusión de una capa de atención. Los resultados mostraron un 85% de precisión y una pérdida del 15% en la clasificación de pacientes normales, DCL’s y demencias. Se concluye que el modelo tiene un buen porcentaje de clasificación y su optimización puede mejorar considerablemente su rendimiento para poder ser implementado enla Atención Primaria (AP) como apoyo en el diagnóstico de deterioro cognitivo
dc.description.abstractenglishThe Rey Complex Figure (RCF) is widely used for the assessment and diagnosis of cognitive impairment (CI). In recent years, automated systems based on Convolutional Neural Networks (CNN) have emerged, which seek to evaluate the RCF as a support tool in the diagnosis of Mild Cognitive Impairment (MCI) and dementia, with positive results in accuracy. Therefore, the aim of this study was to validate an RNC model that uses the FCR in the copying phase, for the classification of MCI and dementia. A total of 1,593 images were obtained, of which 547 were from patients with normal, 655 with MCI and 379 with dementia. The model was developed with Deep Learning (DL), with the inclusion of an attention layer. The results showed 85% accuracy and a 15% loss in the classification of normal, MCI and dementia patients. It is concluded that the model has a good classification rate, and its optimization can considerably improve its performance to be implemented in Primary Care (PC) as a support in the diagnosis of cognitive impairment.
dc.description.degreelevelPregradospa
dc.description.degreenamePsicólogospa
dc.description.sponsorshipFundación Universitaria de Ciencias de la Salud (FUCS)
dc.format.mimetypeapplication/pdf
dc.identifier.instnameinstname:Universidad El Bosquespa
dc.identifier.reponamereponame:Repositorio Institucional Universidad El Bosquespa
dc.identifier.repourlrepourl:https://repositorio.unbosque.edu.co
dc.identifier.urihttps://hdl.handle.net/20.500.12495/13220
dc.language.isoes
dc.publisher.facultyFacultad de Psicologíaspa
dc.publisher.grantorUniversidad El Bosquespa
dc.publisher.programPsicologíaspa
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dc.relation.referencesYoun, Y. C., Pyun, J.-M., Ryu, N., Baek, M. J., Jang, J.-W., Park, Y. H., Ahn, S.-W., Shin, H.-W., Park, K.-Y., & Kim, S. Y. (2021). Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment. Alzheimer’s Research & Therapy, 13(1), 85. https://doi.org/10.1186/s13195-021-00821-8
dc.relation.referencesZhang, X., Lv, L., Min, G., Wang, Q., Zhao, Y., & Li, Y. (2021). Overview of the Complex Figure Test and Its Clinical Application in Neuropsychiatric Disorders, Including Copying and Recall. Frontiers in Neurology, 12. https://doi.org/10.3389/fneur.2021.680474
dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacionallen
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.accessrightshttp:/purl.org/coar/access_right/c_abf2/
dc.rights.localAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.source.urlhttps://vimeo.com/10 2359454
dc.subjectFigura compleja de rey
dc.subjectInteligencia artificial
dc.subjectDeterioro cognitivo
dc.subject.ddc150
dc.subject.keywordsRey complex figure
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsCognitive impairment
dc.titleLa figura de rey como instrumento de clasificación de deterioro cognitivo con el apoyo de inteligencia artificial
dc.title.translatedComplex rey figure as an instrument of classification of cognitive impairment with artificial intelligence support
dc.type.coarhttps://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttps://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.localTesis/Trabajo de grado - Monografía - Pregradospa

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