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Calà F, Frassineti L, Sforza E, Onesimo R, D’Alatri L, Manfredi C, Lanata A, Zampino G. Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics. Bioengineering (Basel) 2023; 10:1375. [PMID: 38135966 PMCID: PMC10741055 DOI: 10.3390/bioengineering10121375] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
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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Affiliation(s)
- Federico Calà
- Department of Information Engineering, University of Florence, 50139 Florence, Italy; (F.C.); (L.F.); (A.L.)
| | - Lorenzo Frassineti
- Department of Information Engineering, University of Florence, 50139 Florence, Italy; (F.C.); (L.F.); (A.L.)
- Department of Information Engineering, Università degli Studi di Pisa, 56122 Pisa, Italy
| | - Elisabetta Sforza
- Department of Life Sciences and Public Health, Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (E.S.); (G.Z.)
| | - Roberta Onesimo
- Centre for Rare Diseases and Transition, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Lucia D’Alatri
- Unit for Ear, Nose and Throat Medicine, Department of Neuroscience, Sensory Organs and Chest, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Claudia Manfredi
- Department of Information Engineering, University of Florence, 50139 Florence, Italy; (F.C.); (L.F.); (A.L.)
| | - Antonio Lanata
- Department of Information Engineering, University of Florence, 50139 Florence, Italy; (F.C.); (L.F.); (A.L.)
| | - Giuseppe Zampino
- Department of Life Sciences and Public Health, Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (E.S.); (G.Z.)
- Centre for Rare Diseases and Transition, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- European Reference Network for Rare Malformation Syndromes, Intellectual and Other Neurodevelopmental Disorders—ERN ITHACA
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