Brain Neurorehabil.  2022 Nov;15(3):e26. 10.12786/bn.2022.15.e26.

Use of Machine Learning in Stroke Rehabilitation: A Narrative Review

• Choo YJ
• Chang MC

• KMID: 2538361
• DOI: http://doi.org/10.12786/bn.2022.15.e26

Abstract

A narrative review was conducted of machine learning applications and research in the field of stroke rehabilitation. The machine learning models commonly used in medical research include random forest, logistic regression, and deep neural networks. Convolutional neural networks (CNNs), a type of deep neural network, are typically used for image analysis. Machine learning has been used in stroke rehabilitation to predict recovery of motor function using a large amount of clinical data as input. Recent studies on predicting motor function have trained CNN models using magnetic resonance images as input data together with clinical data to increase the accuracy of motor function prediction models. Additionally, a model interpreting videofluoroscopic swallowing studies was developed and investigated. In the future, we anticipate that machine learning will be actively used to treat stroke patients, such as predicting the occurrence of depression and the recovery of language, cognitive, and sensory function, as well as prescribing appropriate rehabilitation treatments.

Keyword

Machine Learning; Artificial Intelligence; Stroke; Rehabilitation; Deep Learning