Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/10749357.2021.1926149 http://hdl.handle.net/11449/210434 |
Resumo: | Introduction: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. Methods: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Results: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. Conclusion: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation. |
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Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluationStrokeartificial intelligencemachine learningrehabilitationIntroduction: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. Methods: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Results: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. Conclusion: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation.Univ Fed Triangulo Mineiro, Dept Appl Phys Therapy, Uberaba, BrazilUniv Fed Triangulo Mineiro, Uberaba, BrazilBotucatu Med Sch, Dept Internal Med, Botucatu, SP, BrazilBotucatu Med Sch, Dept Neurol Psychol & Psychiat, Botucatu, SP, BrazilSao Paulo State Univ, Dept Bioproc & Biotechnol, Botucatu, SP, BrazilSao Paulo State Univ, Dept Bioproc & Biotechnol, Botucatu, SP, BrazilTaylor & Francis LtdUniv Fed Triangulo MineiroBotucatu Med SchUniversidade Estadual Paulista (Unesp)Luvizutto, Gustavo JoseSilva, Gabrielly FernandaNascimento, Monalisa ResendeSousa Santos, Kelly CristinaAppelt, Pablo AndreiMoura Neto, Eduardo deSouza, Juli Thomaz deWincker, Fernanda CristinaMiranda, Luana AparecidaHamamoto Filho, Pedro TadaoSouza, Luciane Aparecida Pascucci Sande deSimoes, Rafael Plana [UNESP]Oliveira Vidal, Edison Iglesias deBazan, Rodrigo2021-06-25T15:20:29Z2021-06-25T15:20:29Z2021-06-12info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16http://dx.doi.org/10.1080/10749357.2021.1926149Topics In Stroke Rehabilitation. Abingdon: Taylor & Francis Ltd, 16 p., 2021.1074-9357http://hdl.handle.net/11449/21043410.1080/10749357.2021.1926149WOS:000660324800001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengTopics In Stroke Rehabilitationinfo:eu-repo/semantics/openAccess2021-10-23T20:17:31Zoai:repositorio.unesp.br:11449/210434Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T20:17:31Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation |
title |
Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation |
spellingShingle |
Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation Luvizutto, Gustavo Jose Stroke artificial intelligence machine learning rehabilitation |
title_short |
Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation |
title_full |
Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation |
title_fullStr |
Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation |
title_full_unstemmed |
Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation |
title_sort |
Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept AI applications for stroke evaluation |
author |
Luvizutto, Gustavo Jose |
author_facet |
Luvizutto, Gustavo Jose Silva, Gabrielly Fernanda Nascimento, Monalisa Resende Sousa Santos, Kelly Cristina Appelt, Pablo Andrei Moura Neto, Eduardo de Souza, Juli Thomaz de Wincker, Fernanda Cristina Miranda, Luana Aparecida Hamamoto Filho, Pedro Tadao Souza, Luciane Aparecida Pascucci Sande de Simoes, Rafael Plana [UNESP] Oliveira Vidal, Edison Iglesias de Bazan, Rodrigo |
author_role |
author |
author2 |
Silva, Gabrielly Fernanda Nascimento, Monalisa Resende Sousa Santos, Kelly Cristina Appelt, Pablo Andrei Moura Neto, Eduardo de Souza, Juli Thomaz de Wincker, Fernanda Cristina Miranda, Luana Aparecida Hamamoto Filho, Pedro Tadao Souza, Luciane Aparecida Pascucci Sande de Simoes, Rafael Plana [UNESP] Oliveira Vidal, Edison Iglesias de Bazan, Rodrigo |
author2_role |
author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Univ Fed Triangulo Mineiro Botucatu Med Sch Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Luvizutto, Gustavo Jose Silva, Gabrielly Fernanda Nascimento, Monalisa Resende Sousa Santos, Kelly Cristina Appelt, Pablo Andrei Moura Neto, Eduardo de Souza, Juli Thomaz de Wincker, Fernanda Cristina Miranda, Luana Aparecida Hamamoto Filho, Pedro Tadao Souza, Luciane Aparecida Pascucci Sande de Simoes, Rafael Plana [UNESP] Oliveira Vidal, Edison Iglesias de Bazan, Rodrigo |
dc.subject.por.fl_str_mv |
Stroke artificial intelligence machine learning rehabilitation |
topic |
Stroke artificial intelligence machine learning rehabilitation |
description |
Introduction: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. Methods: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Results: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. Conclusion: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T15:20:29Z 2021-06-25T15:20:29Z 2021-06-12 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1080/10749357.2021.1926149 Topics In Stroke Rehabilitation. Abingdon: Taylor & Francis Ltd, 16 p., 2021. 1074-9357 http://hdl.handle.net/11449/210434 10.1080/10749357.2021.1926149 WOS:000660324800001 |
url |
http://dx.doi.org/10.1080/10749357.2021.1926149 http://hdl.handle.net/11449/210434 |
identifier_str_mv |
Topics In Stroke Rehabilitation. Abingdon: Taylor & Francis Ltd, 16 p., 2021. 1074-9357 10.1080/10749357.2021.1926149 WOS:000660324800001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Topics In Stroke Rehabilitation |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
16 |
dc.publisher.none.fl_str_mv |
Taylor & Francis Ltd |
publisher.none.fl_str_mv |
Taylor & Francis Ltd |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1797789622157180928 |