A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies

Physiotherapy is a critical area of healthcare that involves the assessment and treatment of physical disabilities and injuries. The use of Artificial Intelligence (AI) in physiotherapy has gained significant attention due to its potential to enhance the accuracy and effectiveness of clinical decisi...

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Detalles Bibliográficos
Autores: Nogales, Alberto, Rodríguez-Aragón, Manuel, García-Tejedor, Álvaro J.
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Málaga
Repositorio:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
Idioma:inglés
OAI Identifier:oai:ddfv.ufv.es:10641/6976
Acceso en línea:https://hdl.handle.net/10641/6976
Access Level:acceso abierto
Palabra clave:Deep learning
Musculoskeletal pathologies
Physiotherapy
Systematic review
Health Informatics
Computer Science Applications
Journal Article
Systematic Review
Research Support, Non-U.S. Gov't
Yes
yes
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spelling A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologiesNogales, AlbertoRodríguez-Aragón, ManuelGarcía-Tejedor, Álvaro J.Deep learningMusculoskeletal pathologiesPhysiotherapySystematic reviewHealth InformaticsComputer Science ApplicationsJournal ArticleSystematic ReviewResearch Support, Non-U.S. Gov'tYesyesPhysiotherapy is a critical area of healthcare that involves the assessment and treatment of physical disabilities and injuries. The use of Artificial Intelligence (AI) in physiotherapy has gained significant attention due to its potential to enhance the accuracy and effectiveness of clinical decision-making and treatment outcomes. Nevertheless, it is still a rather innovative field of application of these techniques and there is a need to find what aspects are highly developed and what possible job niches can be exploited. This systematic review aims to evaluate the current state of research on the use of a particular AI called deep learning models in physiotherapy and identify the key trends, challenges, and opportunities in this field. The findings of this review, conducted following the PRISMA guidelines, provide valuable insights for researchers and clinicians. The most relevant databases included were PubMed, Web of Science, Scopus, Astrophysics Data System, and Central Citation Export. Inclusion and exclusion criteria were established to determine which items would be considered for further review. These criteria were used to screen the items during the first and second peer review processes. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, of the 214 initial papers, 23 studies were selected. From our analysis of the selected articles, we can draw the following conclusions: Concerning deep learning models, innovation is primarily seen in the adoption of hybrid models, with convolutional models being extensively utilized. In terms of data, it's unsurprising that body signals and images are predominantly used. However, texts and structured data present promising avenues for groundbreaking work in the field. Additionally, medical tests that involve the collection of 3D images, Functional Movement Screening, or thermographies emerge as novel areas to explore applications within the scope of physiotherapy.Escuela Politécnica Superior20242024-04-0120242024-04-01review articlehttp://purl.org/coar/resource_type/c_dcae04bcinfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10641/6976reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoriainstname:Universidad de MálagaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddfv.ufv.es:10641/69762026-06-11T12:44:57Z
dc.title.none.fl_str_mv A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies
title A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies
spellingShingle A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies
Nogales, Alberto
Deep learning
Musculoskeletal pathologies
Physiotherapy
Systematic review
Health Informatics
Computer Science Applications
Journal Article
Systematic Review
Research Support, Non-U.S. Gov't
Yes
yes
title_short A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies
title_full A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies
title_fullStr A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies
title_full_unstemmed A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies
title_sort A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies
dc.creator.none.fl_str_mv Nogales, Alberto
Rodríguez-Aragón, Manuel
García-Tejedor, Álvaro J.
author Nogales, Alberto
author_facet Nogales, Alberto
Rodríguez-Aragón, Manuel
García-Tejedor, Álvaro J.
author_role author
author2 Rodríguez-Aragón, Manuel
García-Tejedor, Álvaro J.
author2_role author
author
dc.contributor.none.fl_str_mv Escuela Politécnica Superior
dc.subject.none.fl_str_mv Deep learning
Musculoskeletal pathologies
Physiotherapy
Systematic review
Health Informatics
Computer Science Applications
Journal Article
Systematic Review
Research Support, Non-U.S. Gov't
Yes
yes
topic Deep learning
Musculoskeletal pathologies
Physiotherapy
Systematic review
Health Informatics
Computer Science Applications
Journal Article
Systematic Review
Research Support, Non-U.S. Gov't
Yes
yes
description Physiotherapy is a critical area of healthcare that involves the assessment and treatment of physical disabilities and injuries. The use of Artificial Intelligence (AI) in physiotherapy has gained significant attention due to its potential to enhance the accuracy and effectiveness of clinical decision-making and treatment outcomes. Nevertheless, it is still a rather innovative field of application of these techniques and there is a need to find what aspects are highly developed and what possible job niches can be exploited. This systematic review aims to evaluate the current state of research on the use of a particular AI called deep learning models in physiotherapy and identify the key trends, challenges, and opportunities in this field. The findings of this review, conducted following the PRISMA guidelines, provide valuable insights for researchers and clinicians. The most relevant databases included were PubMed, Web of Science, Scopus, Astrophysics Data System, and Central Citation Export. Inclusion and exclusion criteria were established to determine which items would be considered for further review. These criteria were used to screen the items during the first and second peer review processes. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, of the 214 initial papers, 23 studies were selected. From our analysis of the selected articles, we can draw the following conclusions: Concerning deep learning models, innovation is primarily seen in the adoption of hybrid models, with convolutional models being extensively utilized. In terms of data, it's unsurprising that body signals and images are predominantly used. However, texts and structured data present promising avenues for groundbreaking work in the field. Additionally, medical tests that involve the collection of 3D images, Functional Movement Screening, or thermographies emerge as novel areas to explore applications within the scope of physiotherapy.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-04-01
2024
2024-04-01
dc.type.none.fl_str_mv review article
http://purl.org/coar/resource_type/c_dcae04bc
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10641/6976
url https://hdl.handle.net/10641/6976
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
instname:Universidad de Málaga
instname_str Universidad de Málaga
reponame_str DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
collection DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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