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| Autores principales: | , , , |
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| Formato: | Artículo Open Access |
| Publicado: |
Wiley
2024
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| Materias: | |
| Acceso en línea: | https://aao-hnsfjournals.onlinelibrary.wiley.com/doi/10.1002/ohn.809 |
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- A Scoping Review of Artificial Intelligence Detection of Voice Pathology: Challenges and Opportunities George S. Liu Nedeljko Jovanovic C. Kwang Sung Philip C. Doyle Otolaryngology–Head and Neck Surgery AbstractObjectiveSurvey the current literature on artificial intelligence (AI) applications for detecting and classifying vocal pathology using voice recordings, and identify challenges and opportunities for advancing the field forward.Data SourcesPubMed, EMBASE, CINAHL, and Scopus databases.Review MethodsA comprehensive literature search was performed following the Preferred Reporting Items for Systematic Reviews and Meta‐analyses Extension for Scoping Reviews guidelines. Peer‐reviewed journal articles in the English language were included if they used an AI approach to detect or classify pathological voices using voice recordings from patients diagnosed with vocal pathologies.ResultsEighty‐two studies were included in the review between the years 2000 and 2023, with an increase in publication rate from one study per year in 2012 to 10 per year in 2022. Seventy‐two studies (88%) were aimed at detecting the presence of voice pathology, 24 (29%) at classifying the type of voice pathology present, and 4 (5%) at assessing pathological voice using the Grade, Roughness, Breathiness, Asthenia, and Strain scale. Thirty‐six databases were used to collect and analyze speech samples. Fourteen articles (17%) did not provide information about their AI model validation methodology. Zero studies moved beyond the preclinical and offline AI model development stages. Zero studies specified following a reporting guideline for AI research.ConclusionThere is rising interest in the potential of AI technology to aid the detection and classification of voice pathology. Three challenges—and areas of opportunities—for advancing this research are heterogeneity of databases, lack of clinical validation studies, and inconsistent reporting. 10.1002/ohn.809 http://onlinelibrary.wiley.com/termsAndConditions#vor