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A Deep‐Learning Model for Multi‐class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy

The Laryngoscope(2025)10.1002/lary.32036Source: DataRank Database

A Deep‐Learning Model for Multi‐class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy is a research paper published in The Laryngoscope (2025). On theSindex it has a DataRank of 0.339. It has been cited 7 times, with 5 citing works in its 1-hop citation network.

N/A

0.339DataRank · unranked

Scored on demand from live citation data

Open Access7 citations · 5 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 4.53)OA: bronzeTopics (33)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 92%Citation Network 8%

Base Score Contribution

0.312

From this paper's citation signal

Citation Network Contribution

0.0274

From 2 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 5 citers.

Multimodal laryngoscopic video analysis for assisted diagnosis of vocal fold paralysis
Computer Speech & Language20251 citations42 referencesContributes 0.017
Decoding Gender in Cough Sounds: A Transformer‐Based Analysis
The Laryngoscope20262 citations70 referencesContributes 0.016
Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions
World Journal of Otorhinolaryngology - Head and Neck Surgery20260 citations52 referencesContributes 0.000
Bridging Acoustic and Semantic Spaces for Interpretable Voice Scoring via Zero-Shot Semantic Expansion
medRxiv20260 citations39 referencesContributes 0.000
Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review
Journal of Personalized Medicine20260 citations76 referencesContributes 0.000

Authors (13)

Maria Dobko,Haomiao Li ORCID,Tianlan Shao ORCID,Preethi Periyakoil ORCID,Courtney B. Tipton ORCID

Live enrichment

PubMed:39907244 PMC:PMC12234251 OpenAlex:https://openalex.org/W4407176216

National Institute on Aging

Grant: K76AG079040

Common Fund

Grant: OT2 OD032720

NIGMS NIH HHS

Grant: T32 GM152349

FWCI

4.53

Citation Percentile

0.9%

Citation Trend

2025

2026

bronzeLicense: cc-by-nc-nd

journalhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/lary.32036

publisherhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/lary.32036

publisherhttps://onlinelibrary.wiley.com/doi/pdf/10.1002/lary.32036

journalhttps://doi.org/10.1002/lary.32036

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39907244

Fields of Study

Voice and Speech DisordersDysphagia Assessment and ManagementHead and Neck Cancer Studies

MeSH Terms

Deep LearningAdultAgedFemaleHumansLaryngeal DiseasesLaryngoscopyMaleMiddle AgedRetrospective StudiesVideo RecordingVocal Cord ParalysisVocal CordsStroboscopy

Keywords

StroboscopeClassifier (UML)SpectrogramArtificial intelligenceBinary classificationPhonationMedicineSpeech recognitionComputer scienceAudiologyEngineeringSupport vector machineLaryngologyConvolutional Neural NetworkDeep LearningComputer‐aided Diagnosis

Sustainable Development Goals

SDG 0: Quality Education