Current Technological Advances in… | 2025

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69/100

✓ Peer-reviewed alzhub usefulness score 69 out of 100. Clinical safety

Topic: Risk, prevention and care in population studies

This review included 24 studies involving 2979 participants (1717 with dysphagia and 1262 controls). In total, 75% (18/24) of the studies focused solely on per-individual classification rather than per-swallow event classification. Acoustic (13/24, 54%) and vibratory (9/24, 38%) signals were the primary modality sources. In total, 25% (6/24) of the studies used multimodal approaches, whereas 75% (18/24) used a single modality. Support vector machine was the most common AI model (15/24, 62%), with deep learning approaches emerging in recent years (3/24, 12%). Performance varied widely-accuracy ranged from 71.2% to 99%, area under the receiver operating characteristic curve ranged from 0.77 to 0.977, and sensitivity ranged from 63.6% to 100%. Multimodal systems generally outperformed unimodal systems. The methodological quality assessment revealed a risk of bias, particularly in patient selection (unclear in 18/24, 75% of the studies), index test (unclear in 23/24, 96% of the studies), and modeling (high risk in 13/24, 54% of the studies). Notably, no studies conducted external validation or domain adaptation testing, raising concerns about real-world applicability. This review provides a comprehensive overview of technological advancements in AI and sensor-based dysphagia screening. While these developments show promise for continuous long-term tele-swallowing assessments, significant methodological limitations were identified. Future studies can explore how each modality can target specific anatomical regions and manifestations of dysphagia. This detailed understanding of how different modalities address various aspects of dysphagia can significantly benefit multimodal systems, enabling them to better handle the multifaceted nature of dysphagia conditions.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Wong, D. W., Wang, J., Cheung, S. M.-Y., Lai, D. K.-H., Chiu, A. T. S., Pu, D., et al. (2025). Current Technological Advances in Dysphagia Screening: Systematic Scoping Review. Journal of Medical Internet Research. https://doi.org/10.2196/65551

Vancouver

Wong DW, Wang J, Cheung SM.-Y, Lai DK.-H, Chiu ATS, Pu D, et al. Current Technological Advances in Dysphagia Screening: Systematic Scoping Review. Journal of Medical Internet Research. 2025. doi:10.2196/65551. PMID: 40324167.

BibTeX

@article{wong2025-40324167,
  author  = {Wong, Duo Wai‐Chi and Wang, Jiao and Cheung, Sophia Ming-Yan and Lai, Derek Ka-Hei and Chiu, Armstrong Tat San and Pu, Dai and Cheung, James Chung‐Wai and Kwok, Timothy C. Y.},
  title   = {{Current Technological Advances in Dysphagia Screening: Systematic Scoping Review}},
  journal = {Journal of Medical Internet Research},
  year    = {2025},
  doi     = {10.2196/65551},
  pmid    = {40324167},
  url     = {https://doi.org/10.2196/65551}
}

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.

Key findings

This review included 24 studies involving 2979 participants (1717 with dysphagia and 1262 controls).
In total, 75% (18/24) of the studies focused solely on per-individual classification rather than per-swallow event classification.
Acoustic (13/24, 54%) and vibratory (9/24, 38%) signals were the primary modality sources.
In total, 25% (6/24) of the studies used multimodal approaches, whereas 75% (18/24) used a single modality.
Support vector machine was the most common AI model (15/24, 62%), with deep learning approaches emerging in recent years (3/24, 12%).

Why it matters

This review of 24 studies found that AI and sensor-based dysphagia screening shows promise for continuous long-term tele-swallowing assessments, but no studies conducted external validation or domain adaptation testing. The evidence is limited by risk of bias in patient selection, index test, and modeling, so real-world applicability remains uncertain.

Study details

Study designmeta-analysis
Participants24
Effect size71.2%; 63.6%
Evidence strengthHigh

How this study scored 69/100

Published in a peer-reviewed journal, and it is a meta-analysis pooling many studies.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Journal of Medical Internet Research)
Journal's citation record	3 / 4	Journal of Medical Internet Research: journal h-index 286 (OpenAlex, 2026-10-29)
Study design	12 / 12	A meta-analysis pooling many studies
Strength of evidence	5 / 5	Rated “high” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	1 / 4	24 participants
Effect reported	2 / 3	Effect reported as a number, without an interval or test
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	0 / 2	A review of other studies — randomisation is not credited here
Times cited	3 / 7	Cited 17 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	5.7 citations a year over 3 year(s) since 2025
Impact for its field and year	5 / 6	Cited 23.7× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	2 / 4	Cited 17 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	3 / 4	Last-listed author Timothy C. Y. Kwok: h-index 78 (OpenAlex, whole career)
Institution's track record	1 / 3	Monash University: institution h-index 1,146 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 5 institution(s) in 3 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40324167)
Free to read	2 / 2	Free to read
Total	69 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

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