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Related Experiment Video

Updated: May 26, 2026

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
10:15

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

Published on: July 2, 2013

Machine learning models in post-stroke aphasia: a scoping review.

Xiaoxue Li1, Hengjie Song1,2, Ningjing Guo1

  • 1School of Nursing, Shanxi Medical University, Shanxi, China.

Frontiers in Neurology
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

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Machine learning models show great potential for diagnosing and predicting outcomes in post-stroke aphasia. Further research with diverse data is needed to improve clinical use.

Area of Science:

  • Computational linguistics
  • Neurorehabilitation
  • Artificial intelligence in medicine

Background:

  • Post-stroke aphasia significantly impacts communication and quality of life.
  • Machine learning (ML) offers novel approaches to understand and manage aphasia.

Purpose of the Study:

  • To systematically review ML model applications in post-stroke aphasia.
  • To guide the development and clinical implementation of ML models for aphasia.

Main Methods:

  • Scoping review methodology.
  • Comprehensive literature search across major databases (e.g., Web of Science, PubMed, Embase).
  • Screening, summarization, and analysis of 19 relevant articles.

Main Results:

Keywords:
aphasiamachine learningnursing carescoping reviewstroke

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Published on: July 10, 2014

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Last Updated: May 26, 2026

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
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Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

Published on: July 2, 2013

Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke
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Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke

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  • Supervised ML algorithms (Random Forests, Neural Networks, SVMs) are predominant.
  • Models utilize diverse, multimodal data sources.
  • ML functions include diagnosis, severity assessment, outcome prediction, and symptom monitoring.

    • Conclusions:

    • ML models demonstrate high applicability and broad potential in post-stroke aphasia management.
    • Future research should focus on multi-center, multi-modal data and external validation for enhanced robustness and clinical feasibility.