Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia Article Swipe
Rami Saab
,
Arjun Balachandar
,
Hamza Mahdi
,
Eptehal Nashnoush
,
Lucas X. Perri
,
Ashley Waldron
,
Alireza Sadeghian
,
Gordon D. Rubenfeld
,
Mark Crowley
,
Mark I. Boulos
,
Brian J. Murray
,
Houman Khosravani
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2023.1302132
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2023.1302132
Introduction Post-stroke dysphagia is common and associated with significant morbidity and mortality, rendering bedside screening of significant clinical importance. Using voice as a biomarker coupled with deep learning has the potential to improve patient access to screening and mitigate the subjectivity associated with detecting voice change, a component of several validated screening protocols. Methods In this single-center study, we developed a proof-of-concept model for automated dysphagia screening and evaluated the performance of this model on training and testing cohorts. Patients were admitted to a comprehensive stroke center, where primary English speakers could follow commands without significant aphasia and participated on a rolling basis. The primary outcome was classification either as a pass or fail equivalent using a dysphagia screening test as a label. Voice data was recorded from patients who spoke a standardized set of vowels, words, and sentences from the National Institute of Health Stroke Scale. Seventy patients were recruited and 68 were included in the analysis, with 40 in training and 28 in testing cohorts, respectively. Speech from patients was segmented into 1,579 audio clips, from which 6,655 Mel-spectrogram images were computed and used as inputs for deep-learning models (DenseNet and ConvNext, separately and together). Clip-level and participant-level swallowing status predictions were obtained through a voting method. Results The models demonstrated clip-level dysphagia screening sensitivity of 71% and specificity of 77% (F1 = 0.73, AUC = 0.80 [95% CI: 0.78–0.82]). At the participant level, the sensitivity and specificity were 89 and 79%, respectively (F1 = 0.81, AUC = 0.91 [95% CI: 0.77–1.05]). Discussion This study is the first to demonstrate the feasibility of applying deep learning to classify vocalizations to detect post-stroke dysphagia. Our findings suggest potential for enhancing dysphagia screening in clinical settings. https://github.com/UofTNeurology/masa-open-source .
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2023.1302132
- OA Status
- gold
- Cited By
- 9
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388975157
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388975157Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnins.2023.1302132Digital Object Identifier
- Title
-
Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagiaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-24Full publication date if available
- Authors
-
Rami Saab, Arjun Balachandar, Hamza Mahdi, Eptehal Nashnoush, Lucas X. Perri, Ashley Waldron, Alireza Sadeghian, Gordon D. Rubenfeld, Mark Crowley, Mark I. Boulos, Brian J. Murray, Houman KhosravaniList of authors in order
- Landing page
-
https://doi.org/10.3389/fnins.2023.1302132Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fnins.2023.1302132Direct OA link when available
- Concepts
-
Dysphagia, Swallowing, Stroke (engine), Physical medicine and rehabilitation, Medicine, Deep learning, Physical therapy, Computer science, Artificial intelligence, Surgery, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.71% | 218 |
| abstract_inverted_index.77% | 222 |
| abstract_inverted_index.AUC | 226, 248 |
| abstract_inverted_index.CI: | 230, 252 |
| abstract_inverted_index.Our | 275 |
| abstract_inverted_index.The | 103, 210 |
| abstract_inverted_index.and | 5, 10, 37, 67, 76, 97, 137, 151, 162, 184, 192, 195, 198, 219, 238, 242 |
| abstract_inverted_index.for | 63, 188, 279 |
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| abstract_inverted_index.set | 133 |
| abstract_inverted_index.the | 29, 39, 69, 140, 156, 233, 236, 258, 262 |
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| abstract_inverted_index.who | 129 |
| abstract_inverted_index.0.80 | 228 |
| abstract_inverted_index.0.91 | 250 |
| abstract_inverted_index.79%, | 243 |
| abstract_inverted_index.This | 255 |
| abstract_inverted_index.[95% | 229, 251 |
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| abstract_inverted_index.deep | 26, 266 |
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| abstract_inverted_index.0.81, | 247 |
| abstract_inverted_index.1,579 | 174 |
| abstract_inverted_index.6,655 | 179 |
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| abstract_inverted_index.Voice | 123 |
| abstract_inverted_index.audio | 175 |
| abstract_inverted_index.could | 91 |
| abstract_inverted_index.first | 259 |
| abstract_inverted_index.model | 62, 73 |
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| abstract_inverted_index.study | 256 |
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| abstract_inverted_index.which | 178 |
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| abstract_inverted_index.Scale. | 146 |
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| abstract_inverted_index.Stroke | 145 |
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| abstract_inverted_index.basis. | 102 |
| abstract_inverted_index.clips, | 176 |
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| abstract_inverted_index.images | 181 |
| abstract_inverted_index.inputs | 187 |
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| abstract_inverted_index.level, | 235 |
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| abstract_inverted_index.rolling | 101 |
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| abstract_inverted_index.suggest | 277 |
| abstract_inverted_index.testing | 77, 165 |
| abstract_inverted_index.through | 205 |
| abstract_inverted_index.vowels, | 135 |
| abstract_inverted_index.without | 94 |
| abstract_inverted_index.National | 141 |
| abstract_inverted_index.Patients | 79 |
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| abstract_inverted_index.clinical | 17, 284 |
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| abstract_inverted_index.ConvNext, | 193 |
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| abstract_inverted_index.analysis, | 157 |
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| abstract_inverted_index.potential | 30, 278 |
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| abstract_inverted_index.settings. | 285 |
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| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5005293237 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I1323843004, https://openalex.org/I185261750, https://openalex.org/I2802362269, https://openalex.org/I4210123574 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.94656278 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |