ArticleIEEE journal of translational engineering in health and medicine2020
Determination of Clinical Parameters Sensitive to Functional Voice Disorders Applying Boosted Decision Stumps.
Article in IEEE journal of translational engineering in health and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.Frontiers in artificial intelligence · 2025Article
- Multiparametric analysis of voice following prolonged voice use and voice rest in teachers: evidence from discriminant analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2022Article
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Authors and funding
7 authors.
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Abstract
backgroundVarious voice assessment tools, such as questionnaires and aerodynamic voice characteristics, can be used to assess vocal function of individuals. However, not much is known about the best combinations of these parameters in identification of functional dysphonia in clinical settings.
methodsThis study investigated six scores from clinically commonly used questionnaires and seven acoustic parameters. 514 females and 277 males were analyzed. The subjects were divided into three groups: one healthy group (N
resultsThe questionnaire "Voice-related-quality-of-Life" and three objective parameters ("maximum fundamental frequency", "maximum Intensity" and "Jitter Percent") were sufficient to separate the groups (accuracy ranging from 0.690 (FD
conclusionResults show that single parameters are insufficient to separate voice disorders but a set of several well-chosen parameters is. These findings will help to optimize and reduce clinical assessment time.
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