ArticleScientific reports2022
Post-stroke respiratory complications using machine learning with voice features from mobile devices.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Review
- Screening and Evaluation of Post-stroke Dysphagia: Insights from Neurology, Artificial Intelligence and Data Science-A Scoping Review.Annals of biomedical engineering · 2026Review
- Post-swallowing voice-based aspiration screening in dysphagia using a deep learning approach: insights from audio segmentation.Scientific reports · 2026Article
- Prediction of Aspiration Risk by Using Vocal Biomarkers: Machine Learning Development and Validation Study.JMIR formative research · 2026Article
- The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Review
- Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations.Scientific reports · 2024Article
- Prediction of dysphagia aspiration through machine learning-based analysis of patients' postprandial voices.Journal of neuroengineering and rehabilitation · 2024Observational
- A deep learning approach to dysphagia-aspiration detecting algorithm through pre- and post-swallowing voice changes.Frontiers in bioengineering and biotechnology · 2024Article
- Investigating the Impact of Voice Impairment on Quality of Life in Stroke Patients: The Voice Handicap Index (VHI) Questionnaire Study.Brain & NeuroRehabilitation · 2023Article
- Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.Frontiers in neuroscience · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abnormal voice may identify those at risk of post-stroke aspiration. This study was aimed to determine whether machine learning algorithms with voice recorded via a mobile device can accurately classify those with dysphagia at risk of tube feeding and post-stroke aspiration pneumonia and be used as digital biomarkers. Voice samples from patients referred for swallowing disturbance in a university-affiliated hospital were collected prospectively using a mobile device. Subjects that required tube feeding were further classified to high risk of respiratory complication, based on the voluntary cough strength and abnormal chest x-ray images. A total of 449 samples were obtained, with 234 requiring tube feeding and 113 showing high risk of respiratory complications. The eXtreme gradient boosting multimodal models that included abnormal acoustic features and clinical variables showed high sensitivity levels of 88.7% (95% CI 82.6-94.7) and 84.5% (95% CI 76.9-92.1) in the classification of those at risk of tube feeding and at high risk of respiratory complications; respectively. In both cases, voice features proved to be the strongest contributing factors in these models. Voice features may be considered as viable digital biomarkers in those at risk of respiratory complications related to post-stroke dysphagia.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.