ArticleDigital health
Diagnostic value of a deep learning-based hyoid bone tracking model for aspiration in patients with post-stroke dysphagia.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for aspiration risk in stroke patients: a systematic review.Frontiers in neurologyPooled it
- Variations in Head-Neck Posture and Hyoid Kinematics in Patients With Stroke and Aspiration Risk: A Cross-Sectional Observational Study.Archives of rehabilitation research and clinical translation · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Hyoid bone movement is potentially related to aspiration risk in post-stroke dysphagia (PSD) patients but is difficult to assess quantitatively. This study aimed to measure the distance of hyoid bone movement more efficiently and accurately using a deep learning model and determine the clinical usefulness of the model in PSD patients. Methods: This study included 85 patients with PSD within 6 months from onset. Patients were grouped into an aspiration group ( Results: Compared with the non-aspiration group, the aspiration group showed significant decreases in hyoid bone movement in all directions. The area under the curve of Conclusion: Hyoid bone movement of PSD patients can be measured quantitatively and efficiently using a deep learning model. Deep learning model-based analysis of hyoid bone movement seems to be useful for predicting aspiration risk and the possibility of resuming oral feeding.
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