A preliminary investigation of whether HRCA signals can differentiate between swallows from healthy people and swallows from people with neurodegenerative diseases
Open accessAbstract
High-resolution cervical auscultation (HRCA) is an emerging method for non-invasively assessing swallowing by using acoustic signals from a contact microphone, vibratory signals from an accelerometer, and advanced signal processing and machine learning techniques. HRCA has differentiated between safe and unsafe swallows, predicted components of the Modified Barium Swallow Impairment Profile, and predicted kinematic events of swallowing such as hyoid bone displacement, laryngeal vestibular closure, and upper esophageal sphincter opening with a high degree of accuracy. However, HRCA has not been used to characterize swallow function in specific patient populations. This study investigated the ability of HRCA to differentiate between swallows from healthy people and people with neurodegenerative diseases. We hypothesized that HRCA would differentiate between swallows from healthy people and people with neurodegenerative diseases with a high degree of accuracy. We analyzed 170 swallows from 20 patients with neurodegenerative diseases and 170 swallows from 51 healthy age-matched adults who underwent concurrent video fluoroscopy with non-invasive neck sensors. We used a linear mixed model and several supervised machine learning classifiers that use HRCA signal features and a leave-one-out procedure to differentiate between swallows. Twenty-two HRCA signal features were statistically significant (p < 0.05) for predicting whether swallows were from healthy people or from patients with neurodegenerative diseases. Using the HRCA signal features alone, logistic regression and decision trees classified swallows between the two groups with 99% accuracy, 100% sensitivity, and 99% specificity. This provides preliminary research evidence that HRCA can differentiate swallow function between healthy and patient populations.
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Videofluoroscopy swallowing study (VFSS) was used as the reference examination in most included studies.
“Videofluoroscopy of swallowing was used as the reference examination in most studies.”
From Artificial intelligence in the diagnosis and management of dysphagia: a scoping review (2025) · cites this paper as [28]
The Introduction calls videofluoroscopy one of the gold standards for assessing swallowing physiology, and Methods > Equipment and Procedures reports that both groups in this study underwent VFSS. This is a single study; it does not assess whether VFSS was the reference examination in most studies. (LLM, conf=0.84)
Experimental — accuracy not yet measuredStudies on AI in dysphagia primarily rely on imaging resources such as VFSS for comparative analysis due to its high reliability.
“Studies on AI in dysphagia primarily rely on imaging resources such as VFSS for comparative analysis due to its high reliability(16,23-28).”
From Artificial intelligence in the diagnosis and management of dysphagia: a scoping review (2025) · cites this paper as [28]
The Introduction (page unavailable) calls videofluoroscopy one of the gold standards for assessing swallowing physiology. The Methods: Data Analysis section (page unavailable) uses VFSS image labels as ground truth for evaluating this study’s machine-learning classifiers. The paper does not establish that AI studies in dysphagia primarily rely on VFSS, or that high reliability is their reason for doing so. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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- Authors
- Donohue, C., Khalifa, Y., Perera, S., Sejdić, E., & Coyle, J. L.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Donohue, Cara
- Venue
- Dysphagia
- Published
- 2020-09-05
- Volume
- 36
- Issue
- 4
- Pages
- 635-643
- ISSN
- 0179-051X (ISSN-L)
- 1432-0460
- PMC
- PMC7933315
- OpenAlex
- W3083374281
- Cited by
- 41
- References
- 36
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 6 · 2023: 5 · 2022: 6 · 2021: 6 · 2020: 1
- FWCI
- 6.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Dysphagia (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD092239, R01HD074819)
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Dysphagia Assessment and Management
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- swallowing, neurodegenerative diseases, swallowing assessment, machine learning classification, videofluoroscopy
- MeSH terms
- Adult, Auscultation, Deglutition, Deglutition Disorders, Humans, Neurodegenerative Diseases, Esophageal Sphincter, Upper
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