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.

This study investigates the emotional dynamics from different cultural backgrounds using a multimodal approach that combines Facial Emotion Recognition and Heart Rate Variability (HRV) analysis. A total of 109 participants from Italy, Japan, and Korea (mean age = 24.5 years) played two casual games, namely Snake and Matching Pairs, to investigate cultural differences in emotional and physiological responses. The results revealed distinct cultural patterns in static emotional expression using generalised linear mixed models (GLMMs). The Italian cultural group showed higher levels of positive facial expressions (FE), particularly happiness; the Korean cultural group showed more frequent negative FE, while the Japanese cultural group showed restrained FE, particularly related to fear. Moreover, emotional transitions were analysed using a Markov-inspired continuousstate operator derived from probabilistic FE vectors, which characterised the temporal structure of emotional changes and uncovered systematic cross-cultural and task-dependent differences in emotional latency. These findings show that emotional transitions are shaped by cultural norms and the cognitive demands of the games. Furthermore, integrating FE and HRV features into GLMMs showed that autonomic indices predict performance and vary across game types. Overall, this study provides three key contributions. First, it indicates that FEs of emotion during gameplay differ significantly across cultures, in accordance with cultural display norms. Second, it demonstrates that emotional transitions are dynamic and influenced by game performance, with cultural background shaping these patterns. Third, it identifies cross-cultural differences in physiological responses, specifically bodily signals such as HRV. These findings enhance understanding of how games elicit and regulate emotional and physiological responses, suggesting applications beyond entertainment.

Background: Perceptual analysis has highlighted that the voice characteristics of patients with rare congenital genetic syndromes differ from those of normophonic subjects. In this paper, we describe the voice phenotype, also called the phonotype, of patients with Crisponi/cold-induced sweating syndrome type 1 (CS/CISS1). Methods: We conducted an observational study at the Department of Life Sciences and Public Health, Rome. Thirteen patients were included in this study (five males; mean age: 16 years; SD: 10.63 years; median age: 12 years; age range: 6–44 years), and five were adults (38%). We prospectively recorded and analyzed acoustical features of three corner vowels [a], [i], and [u]. For perceptual analysis, the GIRBAS (grade, instability, roughness, breathiness, asthenia, and strain) scale was utilized. Acoustic analysis was performed through BioVoice software. Results: We found that CS/CISS1 patients share a common phonotype characterized by articulation disorders and hyper-rhinophonia. Conclusions: This study contributes to delineating the voice of CS/CISS1 syndrome. The phonotype can represent one of the earliest indicators for detecting rare congenital conditions, enabling specialists to reduce diagnosis time and better define a spectrum of rare and ultra-rare diseases.

The early detection of neurodevelopmental disorders in newborns is of utmost importance in clinical practice. Recently, to predict the neurodevelopment scores in preterms, Artificial Intelligence (AI) methods have been proposed mainly based on Electroencephalographic (EEG) or heart rate variability (HRV) analysis. In this work, HRV measures of preterm newborns with and without Sepsis are computed and used as input features of AI regression models. The study assesses the reliability of such features in predicting BAYLEY-III scores obtained during the clinical follow-up at 6- and 12-months. Forty-eight preterms (gestational age $27.8{\pm }1.8$ weeks) were involved, 27 of which were diagnosed with Sepsis. HRV analysis was performed on ECG signals recorded at the corrected term age. BAYLEY-III score prediction was implemented, considering HRV features as input predictors of ensemble regression models. Models were validated using the Leave-One-Subject-Out (LOSO) framework. Encouraging results were achieved, with a Mean Absolute Error (MAE) < 5 points for the Sepsis group in the BAYLEY-III cognitive and language scales at 6- and 12-months. Preliminary results suggested that the autonomic nervous system development may be linked to central nervous system maturation. HRV features, and AI regression models could predict alterations that affect the correct neurodevelopment of newborns.

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 < 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.