Search Results (420)

Objectives: To investigate the reliability and validity of the Singer Reflux Symptom Score (sRSS), a new patient-reported outcome questionnaire documenting the severity of reflux symptoms in singing voice is proposed. Methods: Amateur and professional singers consulting the European Reflux Clinic for laryngopharyngeal reflux disease (LPRD) symptoms and findings were prospectively recruited from January 2022 to February 2023. The diagnosis was based on a Reflux Symptom Score (RSS) > 13 and Reflux Sign Assessment (RSA) > 14. A control group of asymptomatic singer subjects was recruited from the University of Mons. The sRSS was rated within a 7-day period to assess test–retest reliability. Internal consistency was measured using Cronbach’s α in patients and controls. A correlation analysis was performed between sRSS and Singing Voice Handicap Index (sVHI) to evaluate convergent validity. Responsiveness to change was evaluated through pre- to post-treatment sRSS changes. The sRSS threshold for suggesting a significant impact of LPRD on singing voice was determined by receiver operating characteristic (ROC) analysis. Results: Thirty-three singers with suspected LPRD (51.5% female; mean age: 51.8 ± 17.2 years) were consecutively recruited. Difficulty reaching high notes and vocal fatigue were the most prevalent LPRD-related singing complaints. The sRSS demonstrated high internal consistency (Cronbach-α = 0.832), test–retest reliability, and external validity (correlation with sVHI: r = 0.654; p = 0.015). Singers with suspected LPRD reported a significant higher sRSS compared to 68 controls. sRSS item and total scores significantly reduced from pre-treatment to 3 months post-treatment except for the abnormal voice breathiness item. ROC analysis revealed superior diagnostic accuracy for sRSS (AUC = 0.971) compared to sRSS-quality of life (AUC = 0.926), with an optimal cutoff at sRSS > 38.5 (sensitivity: 90.3%; specificity: 85.0%). Conclusions: The sRSS is a reliable and valid singer-reported outcome questionnaire for documenting singing symptoms associated with LPRD leading to personalized management of Singers. Future large-cohort studies are needed to evaluate its specificity for LPRD compared to other vocal fold disorders in singers. Full article

Background/Objective: Puberphonia is a voice disorder characterized by the persistence of a high-pitched voice in sexually mature males. In phoniatrics and speech-language pathology, it is also known as post-mutational voice instability, mutational falsetto, persistent fistulous voice, or functional falsetto. The absence of an age-appropriate vocal pitch may adversely affect psychological well-being and hinder personal, social, and occupational functioning. The aim of this study was to evaluate of the impact of phoniatric and logopedic rehabilitation on voice quality in patients with puberphonia. Methods: The study included 18 male patients, aged 16 to 34 years, rehabilitated for voice mutation disorders. Phoniatric and logopedic rehabilitation included voice therapy tailored to each subject. A logopedist led exercises aimed at lowering and stabilizing the pitch of the voice and improving its quality. A phoniatrician supervised the therapy, monitoring the condition of the vocal apparatus and providing additional diagnostic and therapeutic recommendations as needed. The duration and intensity of the therapy were adjusted for each patient. Before and after voice rehabilitation, the subjects completed the following questionnaires: the Voice Handicap Index (VHI), the Vocal Tract Discomfort (VTD) scale, and the Voice-Related Quality of Life (V-RQOL). They also underwent an acoustic voice analysis. Results: Statistical analysis of the VHI, VTD, and V-RQOL scores, as well as the voice’s acoustic parameters, showed statistically significant differences before and after rehabilitation (p < 0.005). Conclusions: Phoniatric and logopedic rehabilitation is an effective method of reducing and maintaining a stable, euphonic male voice in patients with functional puberphonia. Effective voice therapy positively impacts selected aspects of psychosocial functioning reported by patients, improves voice-related quality of life, and reduces physical discomfort in the vocal tract. Full article

Patient involvement in health technology assessment (HTA) processes is increasingly recognized as pivotal for informed, equitable, and patient-relevant health care decision-making. With the implementation of Joint Scientific Consultations (JSCs) and Joint Clinical Assessments (JCAs) under Regulation (EU) 2021/2282, the European Union has a unique opportunity to design harmonized mechanisms that reflect best practices from established HTA systems. This article, drawing on the Acute Leukemia Advocates Network (ALAN)’s comparative analysis of HTA practices across seven countries (Canada, England, Scotland, France, Germany, Spain, and Italy), examines how current patient involvement processes can inform the JCA framework. It identifies opportunities to replicate effective practices and proposes strategies to embed patient voices meaningfully into the JCA process. By prioritizing robust and inclusive patient involvement, the EU can establish a global benchmark for impactful and consistent HTA processes. By leveraging lessons from international HTA systems and prioritizing clear frameworks, early involvement, and capacity building, the EU can set a global standard for meaningful patient participation in HTA processes. ALAN is an independent global network of patient organizations dedicated to improving outcomes for patients with acute leukemia. Full article

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. Full article

In the wide range of human diseases, Parkinson’s disease (PD) has a high incidence, according to a recent survey by the World Health Organization (WHO). According to WHO records, this chronic disease has affected approximately 10 million people worldwide. Patients who do not receive an early diagnosis may develop an incurable neurological disorder. PD is a degenerative disorder of the brain, characterized by the impairment of the nigrostriatal system. A wide range of symptoms of motor and non-motor impairment accompanies this disorder. By using new technology, the PD is detected through speech signals of the PD victims by using the reduced instruction set computing 5th version (RISC-V) processor. The RISC-V microcontroller unit (MCU) was designed for the voice-controlled human-machine interface (HMI). With the help of signal processing and feature extraction methods, the digital signal is impaired by the impairment of the nigrostriatal system. These speech signals can be classified through classifier modules. A wide range of classifier modules are used to classify the speech signals as normal or abnormal to identify PD. We use Matrix Laboratory (MATLAB R2021a_v9.10.0.1602886) to analyze the data, develop algorithms, create modules, and develop the RISC-V processor for embedded implementation. Machine learning (ML) techniques are also used to extract features such as pitch, tremor, and Mel-frequency cepstral coefficients (MFCCs). Full article

Background and Clinical Significance: Avatar Therapy (AT) for individuals with treatment-resistant auditory verbal hallucinations (AVHs) in schizophrenia aims to address emotional responses, beliefs about voices, self-perception, and coping strategies. This study focuses on three participants who, during AT, shifted their belief about the origin of their most distressing voice from an external source to a self-generated one. Case Presentation: The objective of this study was to explore the evolution of the reattribution of the participants’ most distressing voice to oneself during AT and the patients’ perception of this reattribution. Immersive sessions and semi-structured interviews were transcribed and qualitatively described to provide a session-by-session account of the evolution of each participant’s AVH reattribution to themselves during the course of AT, along with their perceptions of this reattribution. This process led to the recognition that initially perceived as external voices were internally generated thoughts, reflecting how participants viewed themselves. Two participants reported a reduction in AVH severity. All three described positive changes in how they related to their voices and self-perception. Additional improvements were observed in emotional regulation, social functioning, and engagement in personal projects. Conclusions: This reassignment of the voice from an external source to an internal one suggests that AT can modify how individuals relate to their voices and may empower them to regain control over their hallucinations. However, given the exploratory nature of this study, the results should be interpreted as examples. Full article

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several non-motor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years before the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). Methods: Our study was based on cross-sectional study to analysis voice impairment. A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Cross-validation technique was applied to ensure the reliability of performance metrics on train and test subsets. These metrics (accuracy, recall, and precision), help determine the most effective models for distinguishing PD from healthy subjects. Result: Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. Although other models, such as support vector machine (SVM), could be considered with an accuracy of 75.29% and a ROC-AUC score of 82.63%, RF was by far the best one when evaluated across all metrics. The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. Conclusions: This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector. Full article

Background/Objectives: Patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) may report abnormalities in voice and speech; however, no formal research has been conducted in this area. Methods: An online mixed-methods survey was completed by 685 people with ME/CFS. A total of 302 respondents completed the qualitative component (44.09%). Questions assessed disease experience with ME/CFS and post-exertional malaise without prompting on specific symptoms. Within the qualitative results, a search of the terms “speech, voice,” “words,” and “speak” was conducted. Results: Excluding neurocognitive associations, colloquial phrases, and “speech therapy,” there were 38 mentions of the terms in the context of voice or speech changes across 28 unique qualitative survey responses (9.27%). Conclusions: A notable portion of respondents reported voice or speech changes when responding to open-ended qualitative questions about their disease experience. More research is needed regarding the implications of voice and speech anomalies in ME/CFS pathology and disease monitoring. Full article

Objectives: This study aimed to endoscopically assess nasal patency in terms of adenoid obstruction and its mucous coverage, as well as nasal obstruction caused by the inferior nasal turbinate in children with vocal fold nodules. Methods: A retrospective study was conducted involving 54 children admitted to an ENT clinic due to hoarseness caused by vocal fold nodules from 2022 to 2024. The study analyzed medical history, the results of performed flexible nasofiberoscopy and tympanometry. Results: Children with vocal fold nodules snored and slept with open mouths less frequently than the control group of other patients admitted to the ENT outpatient clinic without voice disorders (p = 0.003 and 0.004, respectively). Pathological mucous coverage of the adenoid was observed more often (p = 0.02). The mean adenoid size in the A/C ratio was 52.1% compared to 63.4% in the control group (p = 0.01). Conclusions: Children with vocal fold nodules typically have smaller adenoids, fewer incidents of snoring and open-mouth breathing, but more frequent pathological nasal mucus. It was not possible to prove that the incorrect breathing path through the mouth, causing reduced humidity of the inhaled air, affects the formation of vocal fold nodules. Full article

Laryngeal cancer significantly affects not only survival but also core functions such as speech, swallowing, and breathing. These impairments often result in substantial psychological distress and reduced health-related quality of life (HRQoL). This review aims to synthesize current evidence regarding the psychological impact, quality of life outcomes, and system-level challenges faced by laryngeal cancer patients while identifying strategies for integrated survivorship care. Anxiety and depressive symptoms are highly prevalent among laryngeal cancer patients, particularly those undergoing total laryngectomy or chemoradiotherapy. HRQoL outcomes vary significantly depending on treatment modality, with long-term deficits noted in domains such as voice, swallowing, and emotional well-being. Access to psychological support and rehabilitation remains inconsistent, hindered by institutional, socioeconomic, and cultural barriers. Structured survivorship models, psychological screening, and patient-centered rehabilitation have demonstrated benefits but are not universally implemented. Comprehensive care for laryngeal cancer must extend beyond tumor control to address persistent functional and psychological sequelae. A multidisciplinary, anticipatory, and personalized approach—centered on integrated rehabilitation and mental health support—is essential to optimize survivorship outcomes and improve long-term quality of life. Full article

Background: Asthma is the most common chronic respiratory disease in children and adolescents, associated with high morbidity and healthcare costs. Telemedicine has emerged as a strategy to improve access to care, adherence to treatment and symptom control. However, its effectiveness in the pediatric population has not been clearly studied. Objective: To assess the clinical effectiveness of telemedicine interventions in asthma control and health-related quality of life in asthmatic children and adolescents. Methodology: A systematic review and meta-analysis were performed following PRISMA-2020 guidelines, with previous registration in PROSPERO (CRD42025251000837). Sixteen randomized clinical trials (n = 2642) with patients aged 2–18 years were included. The interventions included videoconferencing, mobile applications, web systems, interactive voice response and mobile units in schools. The outcomes were measured using validated quality-of-life (PAQLQ) and asthma control (ACT/c-ACT) questionnaires. Results: Seven studies were incorporated into the PAQLQ meta-analysis, whose overall effect was non-significant (mean difference = 0.06; 95% CI: −0.06 to 0.18; I² = 5.7%). Five studies provided ACT/c-ACT data, showing a significant effect in favor of telemedicine (mean difference = 0.61; 95% CI: 0.32 to 0.90; I² = 73%). Complementary qualitative analysis revealed improvements in adherence, reduction in exacerbations, emergency department visits and use of rescue medication. Conclusions: Telemedicine improves the clinical control of pediatric asthma, although its impact on the quality of life is limited. Multicenter trials with long-term follow-up and cost-effectiveness evaluation are required. Full article

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice samples were collected under standardized conditions, ensuring data reliability and diversity. The proposed ternary classification algorithm integrates the predictions of binary CNN classifiers through a majority voting ensemble strategy. ResNet, DenseNet, and EfficientNet architectures were employed for model development. The experimental results show that the ensemble model based on ResNet achieves a weighted F1 score of 91.31%, demonstrating superior performance compared to existing approaches. To the best of our knowledge, this is the first large-scale study to perform three-class classification of neurodegenerative diseases using voice data. Full article

As early detection of voice disorders can significantly improve patients’ situation, the automated detection using Artificial Intelligence techniques can be crucial in various applications in this scope. This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). Three publicly available voice disorder datasets have been utilized, and results have been compared based on Inverted-Generational Distance, Hypervolume, spacing, and spread. The results reveal that NSGA-II with the MLP algorithm attained the best convergence and performance. Further, the conformal prediction is leveraged to quantify uncertainty in the feature-selected models, ensuring statistically valid confidence intervals for predictions. Full article

The middle construction (MC) is a term originally used to account for derived intransitives in the generative tradition and is well-documented in many Indo-European languages. While diverse views exist on the Chinese MC, some scholars have argued that Chinese [patient-subj V-qilai AP] sentences share traits with the English MC. Although diachronic literature on the development of the Chinese MC is limited, examining the grammaticalization of directional verb phrases like V-qilai provides valuable insights into its evolution. Building on previous analyses, I identify [patient-subj V-qilai AP] as the Chinese MC with V-qilai as its marker. Through an analysis of Classical Chinese data and the approach of constructional assemblies, I propose that the Chinese MC originated from assembled constructions comprising a subject, a directional verb phrase, and a descriptive construction. Over time, these assemblies evolved, with shifts in subject roles, motion interpretation, and descriptive functions, resulting in the emergence of the MC. Quantitative analysis of Classical data indicates the absence of the MC before 1900, suggesting its development through a gradual increase in the frequency of interconnected assemblies. By tracing the development of the Chinese MC, this research enhances our understanding of how middle voice mechanisms emerge across languages. Full article

Objective: This study aimed to explore the emotional, social, and ethical dimensions of early or presymptomatic diagnosis in individuals with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Methods: A total of 118 participants diagnosed with ADPKD were recruited from a tertiary nephrology center in Türkiye. Data were collected via a 22-item structured and open-ended questionnaire. Chi-square and non-parametric statistical tests were used to assess associations between awareness, attitudes, and demographic variables. Results: Although only 10% of participants reported direct disadvantages from their diagnosis, such as difficulties in employment, insurance, or relationships, many voiced concerns about stigma and long-term uncertainties. Genetic awareness was significantly associated with increased likelihood of recommending family screening (p = 0.022), and higher educational attainment correlated with greater disease knowledge (p < 0.01). Despite emotional burden, 71.2% of participants reported adopting lifestyle modifications, and 79.6% expressed willingness to screen their children, though often with ethical hesitation. Conclusions: While early diagnosis of ADPKD may offer clinical benefits, it also introduces complex psychosocial and ethical dilemmas. These findings highlight the importance of integrating patient-centered counseling, clear communication strategies, and supportive policies to ensure informed decision making and mitigate potential harms. Full article