Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos

El diagnóstico de la periodontitis genera diversidad de criterios que puede llevar a que la decisión del clínico sea subjetiva. El Deep learning como aprendizaje automático es una herramienta computarizada que permiten el manejo de la información en forma veraz rápida y oportuna, además de contar co...

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Autores:
Galvis Zambrano, Laura Melissa
Amaris Brujes, Liz Dayana
Galeano Torres, Luis Alberto
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2020
Institución:
Universidad Santo Tomás
Repositorio:
Repositorio Institucional USTA
Idioma:
spa
OAI Identifier:
oai:repository.usta.edu.co:11634/29327
Acceso en línea:
http://hdl.handle.net/11634/29327
Palabra clave:
Bone loss
Periapical radiography
Deep Learning
Clínica dental
Periodoncia
Tecnología dental
Radiodiagnóstico
Periodontitis
Odontología - Toma de decisiones
Pérdida ósea
Radiografía periapical
Deep Learning
Rights
openAccess
License
Atribución-SinDerivadas 2.5 Colombia
id SANTTOMAS2_8f62408726fdb5f559434d2ccffd6690
oai_identifier_str oai:repository.usta.edu.co:11634/29327
network_acronym_str SANTTOMAS2
network_name_str Repositorio Institucional USTA
repository_id_str
dc.title.spa.fl_str_mv Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos
title Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos
spellingShingle Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos
Bone loss
Periapical radiography
Deep Learning
Clínica dental
Periodoncia
Tecnología dental
Radiodiagnóstico
Periodontitis
Odontología - Toma de decisiones
Pérdida ósea
Radiografía periapical
Deep Learning
title_short Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos
title_full Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos
title_fullStr Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos
title_full_unstemmed Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos
title_sort Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos
dc.creator.fl_str_mv Galvis Zambrano, Laura Melissa
Amaris Brujes, Liz Dayana
Galeano Torres, Luis Alberto
dc.contributor.advisor.spa.fl_str_mv Plata González, Julio César
dc.contributor.author.spa.fl_str_mv Galvis Zambrano, Laura Melissa
Amaris Brujes, Liz Dayana
Galeano Torres, Luis Alberto
dc.subject.keyword.spa.fl_str_mv Bone loss
Periapical radiography
Deep Learning
topic Bone loss
Periapical radiography
Deep Learning
Clínica dental
Periodoncia
Tecnología dental
Radiodiagnóstico
Periodontitis
Odontología - Toma de decisiones
Pérdida ósea
Radiografía periapical
Deep Learning
dc.subject.lemb.spa.fl_str_mv Clínica dental
Periodoncia
Tecnología dental
Radiodiagnóstico
Periodontitis
Odontología - Toma de decisiones
dc.subject.proposal.spa.fl_str_mv Pérdida ósea
Radiografía periapical
Deep Learning
description El diagnóstico de la periodontitis genera diversidad de criterios que puede llevar a que la decisión del clínico sea subjetiva. El Deep learning como aprendizaje automático es una herramienta computarizada que permiten el manejo de la información en forma veraz rápida y oportuna, además de contar con un alto grado de confiabilidad y precisión, aportando nuevas perspectivas para el diagnóstico, pronostico y la planificación del tratamiento. Desarrollar un sistema para la interpretación radiográfica periapical digitalizada como apoyo al diagnóstico periodontal basado en Deep Learning: Fase I Criterios e insumos radiográficos. La población de estudio conformada por una totalidad de 727 imágenes diagnósticas digitalizadas (radiografías periapicales) almacenadas en centro radiológico de la USTA en los años 2019-2020. Criterios de exclusión: Imágenes radiográficas periapicales elongadas, espacios alveolares que albergan implantes. 727 imágenes extraídas, correspondieron a 72 sujetos, 45 mujeres (62 %) y 27 hombres (38%), El promedio de dientes aportados por persona fue de 24,5 ± 4,4 dientes, de otro lado, la media de pérdida dental fue de 7,3± 3,3 dientes. Las métricas obtenidas son similares a otros estudios, encontramos así, que los insumos generados en la Fase I son correctos para el uso en la Fase II, es decir, para dar continuidad, para lo cual solo se tienen las observaciones generadas en el balance poblacional (en términos de distribución por sexo) y en el tamaño de la muestra (en términos imágenes radiográficas). Este sistema de red neuronal está desarrollado para identificar dientes en su fase inicial y será de gran ayuda al clínico, pudiendo procesar gran número de imágenes con los criterios específicos apoyando en el diagnóstico de manera eficiente.
publishDate 2020
dc.date.accessioned.spa.fl_str_mv 2020-08-31T19:01:04Z
dc.date.available.spa.fl_str_mv 2020-08-31T19:01:04Z
dc.date.issued.spa.fl_str_mv 2020-07-02
dc.type.local.spa.fl_str_mv Trabajo de grado
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.category.spa.fl_str_mv Formación de Recurso Humano para la Ctel: Trabajo de grado de Especialización
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.drive.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
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dc.identifier.citation.spa.fl_str_mv Galvis Zambrano, L. M., Amaris Brujes, L. D. y Galeano Torres, L. A. (2020). Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos [Tesis de especialización]. Universidad Santo Tomás, Bucaramanga, Colombia
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11634/29327
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad Santo Tomás
dc.identifier.instname.spa.fl_str_mv instname:Universidad Santo Tomás
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.usta.edu.co
identifier_str_mv Galvis Zambrano, L. M., Amaris Brujes, L. D. y Galeano Torres, L. A. (2020). Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos [Tesis de especialización]. Universidad Santo Tomás, Bucaramanga, Colombia
reponame:Repositorio Institucional Universidad Santo Tomás
instname:Universidad Santo Tomás
repourl:https://repository.usta.edu.co
url http://hdl.handle.net/11634/29327
dc.language.iso.spa.fl_str_mv spa
language spa
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spelling Plata González, Julio CésarGalvis Zambrano, Laura MelissaAmaris Brujes, Liz DayanaGaleano Torres, Luis Alberto2020-08-31T19:01:04Z2020-08-31T19:01:04Z2020-07-02Galvis Zambrano, L. M., Amaris Brujes, L. D. y Galeano Torres, L. A. (2020). Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficos [Tesis de especialización]. Universidad Santo Tomás, Bucaramanga, Colombiahttp://hdl.handle.net/11634/29327reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEl diagnóstico de la periodontitis genera diversidad de criterios que puede llevar a que la decisión del clínico sea subjetiva. El Deep learning como aprendizaje automático es una herramienta computarizada que permiten el manejo de la información en forma veraz rápida y oportuna, además de contar con un alto grado de confiabilidad y precisión, aportando nuevas perspectivas para el diagnóstico, pronostico y la planificación del tratamiento. Desarrollar un sistema para la interpretación radiográfica periapical digitalizada como apoyo al diagnóstico periodontal basado en Deep Learning: Fase I Criterios e insumos radiográficos. La población de estudio conformada por una totalidad de 727 imágenes diagnósticas digitalizadas (radiografías periapicales) almacenadas en centro radiológico de la USTA en los años 2019-2020. Criterios de exclusión: Imágenes radiográficas periapicales elongadas, espacios alveolares que albergan implantes. 727 imágenes extraídas, correspondieron a 72 sujetos, 45 mujeres (62 %) y 27 hombres (38%), El promedio de dientes aportados por persona fue de 24,5 ± 4,4 dientes, de otro lado, la media de pérdida dental fue de 7,3± 3,3 dientes. Las métricas obtenidas son similares a otros estudios, encontramos así, que los insumos generados en la Fase I son correctos para el uso en la Fase II, es decir, para dar continuidad, para lo cual solo se tienen las observaciones generadas en el balance poblacional (en términos de distribución por sexo) y en el tamaño de la muestra (en términos imágenes radiográficas). Este sistema de red neuronal está desarrollado para identificar dientes en su fase inicial y será de gran ayuda al clínico, pudiendo procesar gran número de imágenes con los criterios específicos apoyando en el diagnóstico de manera eficiente.The diagnosis of periodontitis generates a variety of criteria that can lead to the clinician's decision being subjective. Deep learning as machine learning is a computerized tool that allows the information to be handled truthfully, quickly and in a timely manner, in addition to having a high degree of reliability and precision, providing new perspectives for diagnosis, prognosis and treatment planning. To develop a system for digitized periapical radiographic interpretation to support periodontal diagnosis based on Deep Learning: Phase I Radiographic criteria and supplies. The study population made up of a total of 727 digitized diagnostic images (periapical radiographs) stored in the USTA radiological center in the years 2019-2020. Exclusion criteria: Elongated periapical radiographic images, alveolar spaces that house implants. 727 images extracted corresponded to 72 subjects, 45 women (62%) and 27 men (38%). The average number of teeth contributed per person was 24.5 ± 4.4 teeth, on the other hand, the mean of dental loss was 7.3 ± 3.3 teeth. The metrics obtained are similar to other studies, thus we found that the inputs generated in Phase I are correct for use in Phase II, that is, to give continuity, for which only the observations generated in the population balance (in terms of sex distribution) and in the sample size (in terms of radiographic images). This neural network system is developed to identify teeth in their initial phase and will be of great help to the clinician, being able to process many images with specific criteria, supporting the diagnosis efficiently.Especialización Periodonciahttp://www.ustabuca.edu.co/ustabmanga/presentacionEspecializaciónapplication/pdfspaUniversidad Santo TomásEspecialización PeriodonciaFacultad de OdontologíaAtribución-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sistema de apoyo diagnóstico periodontal con deep learning para las clínicas odontológicas de la Universidad Santo Tomás, 2020: Fase I - Insumos y criterios radiográficosBone lossPeriapical radiographyDeep LearningClínica dentalPeriodonciaTecnología dentalRadiodiagnósticoPeriodontitisOdontología - Toma de decisionesPérdida óseaRadiografía periapicalDeep LearningTrabajo de gradoinfo:eu-repo/semantics/acceptedVersionFormación de Recurso Humano para la Ctel: Trabajo de grado de Especializaciónhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA Bucaramanga1. 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Quitenssense; 2000.ORIGINAL2020GalvisLaura .pdf2020GalvisLaura .pdfTrabajo de gradoapplication/pdf1006302https://repository.usta.edu.co/bitstream/11634/29327/15/2020GalvisLaura%20.pdf5a5763d1b7c18b436dfed72c88a2e89eMD515open access2020GalvisLaura1.pdf2020GalvisLaura1.pdfCarta de aprobaciónapplication/pdf184326https://repository.usta.edu.co/bitstream/11634/29327/14/2020GalvisLaura1.pdf90e7253b9804af21501fb0878c8d7d05MD514metadata only access2020GalvisLaura2.pdf2020GalvisLaura2.pdfAutorización de publicaciónapplication/pdf229601https://repository.usta.edu.co/bitstream/11634/29327/13/2020GalvisLaura2.pdf9dc58552f52044e60fa20fbaa7dfb872MD513metadata only access2020GalvisLaura3.pdf2020GalvisLaura3.pdfApéndicesapplication/pdf317027https://repository.usta.edu.co/bitstream/11634/29327/7/2020GalvisLaura3.pdfee8c621ab9dbdd7e92dfd3aab6b97708MD57open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repository.usta.edu.co/bitstream/11634/29327/4/license_rdfdab767be7a093b539031785b3bf95490MD54open accessTHUMBNAIL2020GalvisLaura .pdf.jpg2020GalvisLaura .pdf.jpgGenerated Thumbnailimage/jpeg3465https://repository.usta.edu.co/bitstream/11634/29327/17/2020GalvisLaura%20.pdf.jpgda4983c7fb992cfc8eb99ad9d844ba06MD517open access2020GalvisLaura1.pdf.jpg2020GalvisLaura1.pdf.jpgGenerated Thumbnailimage/jpeg4471https://repository.usta.edu.co/bitstream/11634/29327/18/2020GalvisLaura1.pdf.jpge23917c59a6ef00524c463b4f6bb0a96MD518open access2020GalvisLaura2.pdf.jpg2020GalvisLaura2.pdf.jpgGenerated Thumbnailimage/jpeg5209https://repository.usta.edu.co/bitstream/11634/29327/19/2020GalvisLaura2.pdf.jpgd047b2833d9fc22dd410e92ebe657ff7MD519open access2020GalvisLaura3.pdf.jpg2020GalvisLaura3.pdf.jpgGenerated Thumbnailimage/jpeg3669https://repository.usta.edu.co/bitstream/11634/29327/20/2020GalvisLaura3.pdf.jpg4bf2191033e90b2f22603b7e21f6cc6dMD520open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8807https://repository.usta.edu.co/bitstream/11634/29327/16/license.txtaedeaf396fcd827b537c73d23464fc27MD516open access11634/29327oai:repository.usta.edu.co:11634/293272022-10-10 15:08:09.295open accessRepositorio Universidad Santo Tomásrepositorio@usantotomas.edu.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