ArticleIEEE journal of translational engineering in health and medicine2015
Smartphone-Based Real-time Assessment of Swallowing Ability From the Swallowing Sound.
Article in IEEE journal of translational engineering in health and medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.Sensors (Basel, Switzerland) · 2025Pooled it
- Diagnostic validity of methods for assessment of swallowing sounds: a systematic review.Brazilian journal of otorhinolaryngologyPooled it
- Monitoring Spontaneous Swallowing After Tracheostomy Using a Neck-Worn Electronic Stethoscope: A Pilot Study.Journal of clinical medicine · 2026Article
- Multimodal LLM-driven IoT digital healthcare platform for intelligent dysphagia dietary monitoring.Frontiers in nutrition · 2026Article
- A novel image-based algorithm to support future remote assessment of chewing function.Frontiers in dental medicine · 2026Article
- Automated Assessment of Test of Masticating and Swallowing Solids Using a Neck-Worn Electronic Stethoscope: A Pilot Study.Journal of oral rehabilitation · 2025Article
- Parameter analysis using swallowing sounds shows differences in bolus volume, bolus viscosity, sex, and age.Scientific reports · 2025Article
- Application of an artificial intelligence-assisted electronic stethoscope for evaluating swallowing function decline across age groups and sex in Japan.Geriatrics & gerontology international · 2025Article
- Method for Estimating Amount of Saliva Secreted Using a Throat Microphone.Sensors (Basel, Switzerland) · 2025Article
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- Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art.NPJ digital medicine · 2024Review
- Acoustic Analysis of Swallowing of an Experimental Meal of Three Food Textures: A Comparative Aging Study.Dysphagia · 2024Article
- Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database.Sensors (Basel, Switzerland) · 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
- Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review.Cureus · 2023Review
- Review
- AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome.Sensors (Basel, Switzerland) · 2022Article
- Singing Experience Influences RSST Scores.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dysphagia can cause serious challenges to both physical and mental health. Aspiration due to dysphagia is a major health risk that could cause pneumonia and even death. The videofluoroscopic swallow study (VFSS), which is considered the gold standard for the diagnosis of dysphagia, is not widely available, expensive and causes exposure to radiation. The screening tests used for dysphagia need to be carried out by trained staff, and the evaluations are usually non-quantifiable. This paper investigates the development of the Swallowscope, a smartphone-based device and a feasible real-time swallowing sound-processing algorithm for the automatic screening, quantitative evaluation, and the visualisation of swallowing ability. The device can be used during activities of daily life with minimal intervention, making it potentially more capable of capturing aspirations and risky swallow patterns through the continuous monitoring. It also consists of a cloud-based system for the server-side analyzing and automatic sharing of the swallowing sound. The real-time algorithm we developed for the detection of dry and water swallows is based on a template matching approach. We analyzed the wavelet transformation-based spectral characteristics and the temporal characteristics of simultaneous synchronised VFSS and swallowing sound recordings of 25% barium mixed 3-ml water swallows of 70 subjects and the dry or saliva swallowing sound of 15 healthy subjects to establish the parameters of the template. With this algorithm, we achieved an overall detection accuracy of 79.3% (standard error: 4.2%) for the 92 water swallows; and a precision of 83.7% (range: 66.6%-100%) and a recall of 93.9% (range: 72.7%-100%) for the 71 episodes of dry swallows.
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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.