Machine learning models in post-stroke aphasia: a scoping review
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By
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Xiaoxue Li
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Hengjie Song
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Ningjing Guo
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Congmin Kang
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Xiaoyan Gong
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Xinyu Ji
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Jie Zheng
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May 7, 2026
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Clinical Scorecard: Exploring the Use of Machine Learning Approaches in Aphasia Following Stroke: A Scoping Review
At a Glance
| Category | Detail |
|---|
| Condition | Post-stroke aphasia |
| Key Mechanisms | Utilization of machine learning models for diagnosis, categorization, and rehabilitation of aphasia. |
| Target Population | Stroke survivors experiencing aphasia. |
| Care Setting | Clinical settings focusing on rehabilitation and language therapy. |
Key Highlights
- Machine learning techniques include supervised approaches like random forests and neural networks.
- Models evaluate and forecast severity, predict language capabilities, and monitor symptoms.
- Approximately 30% of stroke survivors experience aphasia, impacting quality of life.
Guideline-Based Recommendations
Diagnosis
- Employ machine learning models for accurate diagnosis and categorization of aphasia.
Management
- Utilize predictive models for individualized rehabilitation strategies.
Monitoring & Follow-up
- Implement machine learning for ongoing assessment of aphasia symptoms.
Risks
- Consider the variability in model performance and the need for external validation.
Patient & Prescribing Data
Individuals with post-stroke aphasia.
Machine learning can enhance the precision of assessments and rehabilitation interventions.
Clinical Best Practices
- Adopt multi-center and multi-modal data approaches for model validation.
- Incorporate machine learning insights into clinical practice for tailored patient care.
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