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Artificial neural networks in neurorehabilitation: A scoping review.
Moon, Sanghee; Ahmadnezhad, Pedram; Song, Hyun-Je; Thompson, Jeffrey; Kipp, Kristof; Akinwuntan, Abiodun E; Devos, Hannes.
Afiliación
  • Moon S; Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.
  • Ahmadnezhad P; Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.
  • Song HJ; Department of Information Technology, Jeonbuk National University, Jeonju, South Korea.
  • Thompson J; Department of Biostatistics, School of Medicine, University of Kansas Medical Center, Kansas City, KS, USA.
  • Kipp K; Department of Physical Therapy, College of Health Sciences, Marquette University, Milwaukee, WI, USA.
  • Akinwuntan AE; Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.
  • Devos H; Office of the Dean, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.
NeuroRehabilitation ; 46(3): 259-269, 2020.
Article en En | MEDLINE | ID: mdl-32250332
BACKGROUND: Advances in medical technology produce highly complex datasets in neurorehabilitation clinics and research laboratories. Artificial neural networks (ANNs) have been utilized to analyze big and complex datasets in various fields, but the use of ANNs in neurorehabilitation is limited. OBJECTIVE: To explore the current use of ANNs in neurorehabilitation. METHODS: PubMed, CINAHL, and Web of Science were used for the literature search. Studies in the scoping review (1) utilized ANNs, (2) examined populations with neurological conditions, and (3) focused on rehabilitation outcomes. The initial search identified 1,136 articles. A total of 19 articles were included. RESULTS: ANNs were used for prediction of functional outcomes and mortality (n = 11) and classification of motor symptoms and cognitive status (n = 8). Most ANN-based models outperformed regression or other machine learning models (n = 11) and showed accurate performance (n = 6; no comparison with other models) in predicting clinical outcomes and accurately classifying different neurological impairments. CONCLUSIONS: This scoping review provides encouraging evidence to use ANNs for clinical decision-making of complex datasets in neurorehabilitation. However, more research is needed to establish the clinical utility of ANNs in diagnosing, monitoring, and rehabilitation of individuals with neurological conditions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Resultado del Tratamiento / Redes Neurales de la Computación / Sistemas de Apoyo a Decisiones Clínicas / Rehabilitación Neurológica Tipo de estudio: Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: NeuroRehabilitation Asunto de la revista: NEUROLOGIA / REABILITACAO Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Resultado del Tratamiento / Redes Neurales de la Computación / Sistemas de Apoyo a Decisiones Clínicas / Rehabilitación Neurológica Tipo de estudio: Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: NeuroRehabilitation Asunto de la revista: NEUROLOGIA / REABILITACAO Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Países Bajos