Voice Characteristics of Children with Noonan Syndrome

Türkyılmaz, Meral Didem; Yilmaz, Serap; Özcebe, Esra; Yüksel, Selcen; Süslü, Nilda; Tekin, Mustafa

doi:10.5336/medsci.2013-35466

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Turkyilmaz et al [9] analyzed the sustained /a/ vowel of 11 children with Noonan syndrome using MVDP software (Kay Elemetrics Corporation, Lincoln Park, NJ, USA); no significant differences were found compared to a control group, except for the soft phonation index (SPI). Moreover, in a single case report, Wilson and Dyson [10] found vowel neutralization and nasalization in a female child.…”

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confidence: 99%

Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics

Calà,

Frassineti,

Sforza

et al. 2023

Bioengineering

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Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith–Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal–Wallis test and post hoc analysis with Dunn–Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes.

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mentioning

confidence: 99%

Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics

Calà,

Frassineti,

Sforza

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

AI-Based Procedure for the Assessment of Voice Characteristics in Genetic Syndromes

Calà,

Frassineti,

Sforza

et al. 2023

Preprint

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Perceptual and statistical evidence has underlined voice characteristics of individuals affected by genetic syndromes different from that of normophonic subjects. In this paper we propose a procedure for the systematic collection of such pathological voices and the development of AI-based automatic tools to support differential diagnosis. Guidelines are provided concerning most suitable recording devices, vocal tasks and acoustical parameters, in order to simplify, speed up and make the whole procedure homogenous and replicable. The proposed procedure was applied to a set of 56 subjects, affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS) and Smith-Magenis syndrome (SMS). The whole database has been divided into three groups, respectively called: paediatric subjects (PS, individuals &lt; 12 years of age), female adults (FA) and male adults (MA) subjects. In line with literature results, the Kruskal-Wallis test and post-hoc analysis with Dunn-Bonferroni test highlighted several significant differences in acoustical features not only between healthy subjects and patients, but also across syndromes within PS, FA and MA groups. Machine learning provided for the PS group a k-nearest neighbour classifier with 75% accuracy, for the FA group a support vector machine (SVM) model with 84% accuracy and for the MA group a SVM model with 97% accuracy. These preliminary results suggest that the proposed procedure, based on acoustical analysis and AI, might be helpful for an effective non-invasive automatic characterization of genetic syndromes.

show abstract

Voice Characteristics of Children with Noonan Syndrome

Cited by 2 publications

References 12 publications

Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics

Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics

AI-Based Procedure for the Assessment of Voice Characteristics in Genetic Syndromes

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