ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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
16 citing papers in PubMed.
- Artificial intelligence tools for the assessment and management of dysphagia: a scoping review.BMJ open · 2026Article
- Human-AI collaboration for dysphagia rehabilitation from effectiveness to implementation complexity: a systematic review.NPJ digital medicine · 2026Article
- Correlation between swallowing and diaphragmatic functions in stroke patients with hemiplegia: a cross-sectional observational study.Scientific reports · 2026Observational
- Multimodal Intelligent Monitoring of Parkinson Disease: Scoping Review of Progress and Translational Challenges.Journal of medical Internet research · 2026Article
- A Multimodal In-Ear Audio and Physiological Dataset for Swallowing and Non-Verbal Event Classification.Sensors (Basel, Switzerland) · 2026Article
- Microscale Architectures for Intelligent Soft Robotics: From Functional Microneedles to Biointegrated Wearable Systems.Nano-micro letters · 2026Review
- Biodegradable MXene-Bamboo Cellulose Paper Electrodes for Green Wearable Sensing and Exoskeleton Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A Machine Learning Pipeline for Automated Bolus Segmentation and Area Measurement in Swallowing Videofluoroscopy Images of an Infant Pig Model.Dysphagia · 2025Article
- Wearable bioelectronics for cancer theranostics.Microsystems & nanoengineering · 2025Review
- Wireless Soft Athlete Bioelectronics for Monitoring Carbon Dioxide Ventilation and Physiological Performance.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Flexible Smart Insole and Plantar Pressure Monitoring Using Screen-Printed Nanomaterials and Piezoresistive Sensors.ACS applied materials & interfaces · 2025Article
- FromMaterials today. Bio · 2025Review
- Current Technological Advances in Dysphagia Screening: Systematic Scoping Review.Journal of medical Internet research · 2025Article
- Multimodal deep ensemble classification system with wearable vibration sensor for detecting throat-related events.NPJ digital medicine · 2025Article
- Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Application of intelligent technologies for dysphagia risk prediction: A scoping review.Digital healthReview
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.
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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.