Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke

Abstract We investigated the potential of machine learning techniques, at an early stage after stroke, to predict the need for ankle–foot orthosis (AFO) in stroke patients. We retrospectively recruited 474 consecutive stroke patients. The need for AFO during ambulation (output variable) was classifi...

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Autores principales:	Yoo Jin Choo, Jeoung Kun Kim, Jang Hwan Kim, Min Cheol Chang, Donghwi Park
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b7bde29910ea445fa0386bee38b81750
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spelling	oai:doaj.org-article:b7bde29910ea445fa0386bee38b817502021-12-02T13:44:14ZMachine learning analysis to predict the need for ankle foot orthosis in patients with stroke10.1038/s41598-021-87826-32045-2322https://doaj.org/article/b7bde29910ea445fa0386bee38b817502021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87826-3https://doaj.org/toc/2045-2322Abstract We investigated the potential of machine learning techniques, at an early stage after stroke, to predict the need for ankle–foot orthosis (AFO) in stroke patients. We retrospectively recruited 474 consecutive stroke patients. The need for AFO during ambulation (output variable) was classified according to the Medical Research Council (MRC) score for the ankle dorsiflexor of the affected limb. Patients with an MRC score of < 3 for the ankle dorsiflexor of the affected side were considered to require AFO, while those with scores ≥ 3 were considered not to require AFO. The following demographic and clinical data collected when patients were transferred to the rehabilitation unit (16.20 ± 6.02 days) and 6 months after stroke onset were used as input data: age, sex, type of stroke (ischemic/hemorrhagic), motor evoked potential data on the tibialis anterior muscle of the affected side, modified Brunnstrom classification, functional ambulation category, MRC score for muscle strength for shoulder abduction, elbow flexion, finger flexion, finger extension, hip flexion, knee extension, and ankle dorsiflexion of the affected side. For the deep neural network model, the area under the curve (AUC) was 0.887. For the random forest and logistic regression models, the AUC was 0.855 and 0.845, respectively. Our findings demonstrate that machine learning algorithms, particularly the deep neural network, are useful for predicting the need for AFO in stroke patients during the recovery phase.Yoo Jin ChooJeoung Kun KimJang Hwan KimMin Cheol ChangDonghwi ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yoo Jin Choo Jeoung Kun Kim Jang Hwan Kim Min Cheol Chang Donghwi Park Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke
description	Abstract We investigated the potential of machine learning techniques, at an early stage after stroke, to predict the need for ankle–foot orthosis (AFO) in stroke patients. We retrospectively recruited 474 consecutive stroke patients. The need for AFO during ambulation (output variable) was classified according to the Medical Research Council (MRC) score for the ankle dorsiflexor of the affected limb. Patients with an MRC score of < 3 for the ankle dorsiflexor of the affected side were considered to require AFO, while those with scores ≥ 3 were considered not to require AFO. The following demographic and clinical data collected when patients were transferred to the rehabilitation unit (16.20 ± 6.02 days) and 6 months after stroke onset were used as input data: age, sex, type of stroke (ischemic/hemorrhagic), motor evoked potential data on the tibialis anterior muscle of the affected side, modified Brunnstrom classification, functional ambulation category, MRC score for muscle strength for shoulder abduction, elbow flexion, finger flexion, finger extension, hip flexion, knee extension, and ankle dorsiflexion of the affected side. For the deep neural network model, the area under the curve (AUC) was 0.887. For the random forest and logistic regression models, the AUC was 0.855 and 0.845, respectively. Our findings demonstrate that machine learning algorithms, particularly the deep neural network, are useful for predicting the need for AFO in stroke patients during the recovery phase.
format	article
author	Yoo Jin Choo Jeoung Kun Kim Jang Hwan Kim Min Cheol Chang Donghwi Park
author_facet	Yoo Jin Choo Jeoung Kun Kim Jang Hwan Kim Min Cheol Chang Donghwi Park
author_sort	Yoo Jin Choo
title	Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke
title_short	Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke
title_full	Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke
title_fullStr	Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke
title_full_unstemmed	Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke
title_sort	machine learning analysis to predict the need for ankle foot orthosis in patients with stroke
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b7bde29910ea445fa0386bee38b81750
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Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke

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