Vocal Disorders Research Articles

Platelet-rich plasma applications in the larynx: snake oil or stupendous?

To explore and summarize recent work examining the use of platelet-rich plasma (PRP) applications for vocal fold disorder. Intrachordal PRP injections have recently emerged as a treatment option for patients with damage to the lamina propria of the vocal fold (scar, atrophy, sulcus and inflammatory lesions). Studies support significant improvements in patient-reported and clinician-reported outcomes, and objective phonatory parameters. PRP demonstrates a good safety profile, absence of immune reactions, affordable cost model and improvement in mucosal wave features with resultant improved voice quality up to 12 months following treatment. PRP injection of the vocal folds is a novel repurposing of a previously validated technique, and emerging data shows consistent results across multiple centres, increasing confidence in use of this freely available material. Current published studies confirm improved voice outcomes compared with pretreatment measures and high patient satisfaction. Further research is required to properly assign the role of and ideal candidate for PRP applications, and to delineate durability of treatment.

Virtues of routine suprahyoid release during tracheal resection and anastomosis in patients with post intubation stenosis.

Postintubation tracheal stenosis is the most common cause of benign tracheal stenosis. Surgical treatment is more challenging in long-segment stenosis. Suprahyoid release can increase tracheal length resected without anastomotic tension in patients with post-intubation tracheal stenosis. Its effect on swallowing has not been objectively studied and this article aims to explore its virtues and potential complications in a tertiary center for airway surgery. A prospective cohort study was conducted on forty consecutive patients from June 2020 till December 2023. Patients of both genders had tracheal resection anastomosis surgery with routine suprahyoid muscle release for resected tracheal segment of more than 2.5 cm in length aiming to decrease the anastomotic tension. Within two weeks postoperatively, a video naso-laryngoscope was done on all the patients to detect any vocal cord disorders, then they were examined by video fluoroscopy swallowing study VFSS to detect swallowing problems. Follow up was done for 6 months postoperatively. 40 patients were studied. Twenty-five patients (62.5%) were males. 21 patients (52.5%) had a cricotracheal resection. VFSS was performed on 38 patients (two patients excluded for serious morbidity). Six (15.7%) and four (10.5%) patients had residual semisolid and solid food in the vallecula and pyriform fossa respectively during swallowing. Five patients (13%) out of eight patients with abnormal VFSS had aspiration and dysphagia. Video nasolaryngoscopy was done pre- and post-operatively and showed that 7 patients (17.5%) had unilateral vocal cord paralysis, two of them had the same lesion preoperatively. Two patients developed postoperative anastomotic complications. All symptoms of dysphagia improved within 3 weeks of the procedure and improvement persisted for 6 months. Suprahyoid muscle release had a considerable reversible drawback on the process of swallowing. Its routine use in high-risk patients requiring long segment tracheal resections could be considered.

AI-Enabled Medical Assessment and Assistance for Vocal Disorders: A Comparative Study

Vocal disorders present significant challenges for patients and clinicians, impacting communication and quality of life. The development of artificial intelligence (AI) technologies offers promising possibilities for improving the assessment and management of vocal disorders. This study aims to evaluate the effectiveness and applicability of different AI approaches in this field through a comparative study of AI-enabled medical assessment and assistance for vocal disorders. Various AI techniques, including machine learning algorithms, deep learning models, and natural language processing methods, are explored in the context of diagnosing vocal disorders, planning treatments, and managing patients. The insights gained from this comparative study contribute to understanding the role of AI in transforming healthcare delivery for vocal disorders, highlighting opportunities, challenges, and future directions for utilizing AI to enhance medical assessment and assistance in this specialized field.

Nonlinear dynamics of inverse tapered granular chains for mechanical amplification in voice frequency range.

Here, the dynamics of inverse tapered granular chains, which are alignments of beads with progressively increasing radii, are investigated. These chains are subjected to harmonic excitation by a moving wall and probed at the tapered end. Intermediate taperings using Ni-Ti (metal) and Teflon (plastic) granules are considered. The proposed chain is based on the frequency responses. The incident signal is set between 1 Hz and 3 kHz. The system transmits or filters incident signals at low or high driving frequencies, respectively. For intermediate frequencies, similar to the human voice and most musical instruments, the driving frequency is amplified, presenting a median gain of 24 dB (an increase in energy by a factor of 250) over long durations. A transition map, showing regions of transmission, amplification, and filtration as a function of driving amplitude and frequency, is provided. This system could serve as a starting point for studies on natural voice amplifiers, which are important for addressing hearing loss and vocal disorders, as well as other applications requiring mechanical signal amplification. The findings suggest that a proper granular arrangement could enable the amplification of low or extremely low driving frequencies.

Boron neutron capture therapy as a larynx-preserving treatment for locally recurrent laryngeal carcinoma after conventional radiation therapy: A preliminary report

ObjectiveLaryngeal preservation and a radical cure are the treatment goals for laryngeal carcinoma, and larynx-preserving therapy is generally preferred for early-stage laryngeal carcinoma. When laryngeal carcinoma recurs locally, patients are often forced to undergo total laryngectomy, resulting in loss of vocal function. However, many patients with laryngeal carcinoma who have residual or recurrent disease after radiotherapy wish to preserve their voice. The purpose of this study was to investigate the possibility of using BNCT as a larynx-preserving treatment for residual or recurrent laryngeal carcinomas following radical irradiation. Patients and MethodsThis study included 15 patients who underwent BNCT for residual or recurrent laryngeal carcinoma after radical laryngeal carcinoma irradiation. The number of treatment sessions for all patients was one irradiation. Before BNCT, the recurrent laryngeal carcinoma stage was rT1aN0, rT2N0, rT2N1, rT3N0, rT3N1, and rT4aN0 in one, six, one, three, one, and three patients, respectively. The median maximum tumor diameter before BNCT was 15 mm (8–22 mm). All patients underwent a tracheostomy before BNCT to mitigate the risk of upper airway stenosis due to laryngeal edema after BNCT. Treatment efficacy was evaluated retrospectively using monthly laryngoscopy after BNCT and contrast-enhanced CT scans at 3 months. The safety of treatment was evaluated based on examination findings and interviews with patients. ResultsThe median hospital stay after BNCT was 2 days (1–6). The response rate at three months after BNCT in 15 patients with locally recurrent laryngeal carcinoma was 93.3 %, and the CR rate was 73.3 %. The most frequent adverse event associated with BNCT was laryngeal edema, which occurred in nine patients the day after BNCT. The average course of laryngeal edema peaked on the second day after BNCT and almost recovered after 1 week in all patients. One patient had bilateral vocal fold movement disorders. None had dyspnea because of prophylactic tracheostomy. No grade four or higher adverse events occurred. Other grade 2 adverse events included pharyngeal mucositis, diarrhea, and sore throat. Three months after BNCT, tracheostomy tubes were removed in nine patients, retinal cannulas were placed in three patients, and voice cannulas were placed in three patients. ConclusionsBNCT for locally recurrent laryngeal carcinoma can safely deliver radical irradiation to tumor tissues, even in patients undergoing radical irradiation. BNCT has shown antitumor effects against recurrent laryngeal carcinoma. However, further long-term observations of the treatment outcomes are required.

Cytokine expression profiles in children and adolescents with tic disorders

The etiology of tic disorders (TDs) is not precisely known, although several lines of evidence suggest involvement of the immune system in pathogenesis. Here, we aimed to determine the expression levels of pro-inflammatory and anti-inflammatory cytokines in children with TD and compare them with those of healthy controls. Furthermore, we also evaluated their association with clinical variables in the TD group. Within the study period, 88 children with tic disorders and 111 healthy control children were enrolled. Most children with tic disorders were diagnosed with Tourette’s disorder (n = 47, 53.4%) or persistent motor tic disorder (n = 39, 44.3%), while the remainder (n = 2, 2.3%) were diagnosed with persistent vocal tic disorder. We found that children with tic disorders had significantly elevated levels of IL-1β, TNF-α, IL-6 and IL-4 expression, while we detected lower expression levels of IL-17 in children with tic disorders. Our findings provide a molecular landscape of cytokine expression in children with TD, which may suggest a proinflammatory state not affected by the presence of comorbidity and symptom severity. Delineating the contribution of alterations in the immune system to the pathogenesis of tic disorders may pave the way for better therapeutic interventions.

A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method

In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system’s ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system’s classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field’s capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease.

Utility of Ultrahigh-Resolution Computed Tomography for Laryngeal Reconstructive Surgery.

Unilateral vocal fold paralysis (UVFP) presents as incomplete glottal closure and leads to breathy hoarseness. Various treatments, including laryngeal framework surgery (type 1 thyroplasty [TP1] and arytenoid adduction [AA]), have been devised to correct this condition. Ultrahigh-resolution computed tomography (U-HRCT) allows detailed three-dimensional imaging of the larynx, which aids our understanding of vocal fold motion disorders. This study assessed whether U-HRCT is beneficial for correct diagnosis and surgical planning. The participants were 26 UVFP patients who underwent laryngeal framework surgery (TP1 and/or AA). U-HRCT was used to measure the vocal fold volume (VFV) and level difference (LD). The need to combine AA with TP1 to obtain satisfactory surgical outcomes was evaluated by U-HRCT and various voice function tests. VFV was smaller in paralyzed folds than in unaffected folds. LD correlated strongly with voice parameters and showed high intra-rater and inter-rater reliability. The surgical outcome of the laryngeal framework surgery performed was judged to be excellent for improving voice function. Comparison of LD between the TP1 group and TP1 + AA group indicated that LD is an excellent parameter to determine the need to combine AA with TP1. These findings underscore the value of preoperative U-HRCT, especially LD, in surgical decision-making and afford insights for optimal phonosurgery and individualized intervention. Patients with LD >1.0 mm may benefit from thyroplasty with AA. 3 (case-control study) Laryngoscope, 134:4667-4673, 2024.

Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.

The diagnosis and treatment of vocal fold disorders heavily rely on the use of laryngoscopy. A comprehensive vocal fold diagnosis requires accurate identification of crucial anatomical structures and potential lesions during laryngoscopy observation. However, existing approaches have yet to explore the joint optimization of the decision-making process, including object detection and image classification tasks simultaneously. In this study, we provide a new dataset, VoFoCD, with 1724 laryngology images designed explicitly for object detection and image classification in laryngoscopy images. Images in the VoFoCD dataset are categorized into four classes and comprise six glottic object types. Moreover, we propose a novel Multitask Efficient trAnsformer network for Laryngoscopy (MEAL) to classify vocal fold images and detect glottic landmarks and lesions. To further facilitate interpretability for clinicians, MEAL provides attention maps to visualize important learned regions for explainable artificial intelligence results toward supporting clinical decision-making. We also analyze our model's effectiveness in simulated clinical scenarios where shaking of the laryngoscopy process occurs. The proposed model demonstrates outstanding performance on our VoFoCD dataset. The accuracy for image classification and mean average precision at an intersection over a union threshold of 0.5 (mAP50) for object detection are 0.951 and 0.874, respectively. Our MEAL method integrates global knowledge, encompassing general laryngoscopy image classification, into local features, which refer to distinct anatomical regions of the vocal fold, particularly abnormal regions, including benign and malignant lesions. Our contribution can effectively aid laryngologists in identifying benign or malignant lesions of vocal folds and classifying images in the laryngeal endoscopy process visually.

Knowledge and Needs of Theology Students on Voice and Vocal Hygiene at a South African Institution of Higher Education

Preachers are professional voice users (PVUs) who regularly experience high vocal demands, often leading to vocal difficulties or disorders. Research has established that this often occurs due to a lack of sufficient knowledge of the vocal mechanism and vocal hygiene in this population. Student preachers are therefore also considered to be a potential at-risk group for the development of vocal difficulties once they enter the occupation. This study therefore aimed to determine the knowledge and needs of theology students at a South African institution of higher education regarding voice production and vocal hygiene. A cross-sectional online survey using non-probability purposive and convenience sampling was distributed to participants through email to obtain data on the education, training, needs, and knowledge of voice production, vocal hygiene, and voice use among the participants. A clear need was observed for education and training on vocal health and hygiene amongst the study population. The participants presented with limited knowledge about voice production and vocal hygiene; however, they also reported to not regularly implement this limited knowledge in day-to-day voice use or practice. This study found a willingness among participants to learn more about voice and vocal hygiene. This study confirmed that theology students, as future PVUs, receive minimal-to-no training on voice and vocal hygiene, despite their increased risk for voice difficulties.

A real-world pharmacovigilance study of FDA adverse event reporting system events for Capmatinib

Capmatinib is a potent selective mesenchymal-epithelial transition inhibitor approved in 2020 for the treatment of metastatic non-small cell lung cancer. As real-world evidence is very limited, this study evaluated capmatinib-induced adverse events through data mining of the FDA Adverse Event Reporting System database. Four disproportionality analysis methods were employed to quantify the signals of capmatinib-related adverse events. The difference in capmatinib-associated adverse event signals was further investigated with respect to sex, age, weight, dose, onset time, continent, and concomitant drug. A total of 1518 reports and 4278 adverse events induced by capmatinib were identified. New significant adverse event signals emerged, such as dysphagia, dehydration, deafness, vocal cord paralysis, muscle disorder, and oesophageal stenosis. Notably, higher risk of alanine aminotransferase and aspartate aminotransferase increases were observed in females, especially when capmatinib was combined with immune checkpoint inhibitors. Compared with Europeans and Asians, Americans were more likely to experience peripheral swelling, especially in people > 65 years of age. Renal impairment and increased blood creatinine were more likely to occur with single doses above 400 mg and in Asians. This study improves the understanding of safety profile of capmatinib.

Visualization and Analysis of Vocal Fold Dynamics in Various Voice Disorders

Objectives/HypothesisThis study aims to compare the glottal dynamics of vocal fold leukoplakia, laryngitis, and papilloma using various visualization techniques, including phonovibrograms (PVG) and glottovibrograms (GVG). It hypothesizes that these techniques can provide understanding of the vibratory dynamics of these voice disorders, which can aid in their objective diagnosis and deriving visual features for computer-assisted classifications. Study DesignThis work employs a comparative analyses of vocal fold disorders using visualization techniques. The study examines phonovibrograms (PVG), derivative of PVG (PVG-1), digital kymograms (DKG) and glottovibrograms (GVG) to analyze the glottal dynamics of vocal fold leukoplakia, laryngitis, and papilloma. MethodsThe study utilizes high-speed video (HSV) endoscopy to capture vocal fold behavior in different pathological conditions. PVG, GVG and DKG techniques are applied to visualize and analyze the vibratory patterns of the vocal folds. PVG offers a comprehensive portrayal of vocal fold motion, while GVG presents the distance between vocal fold contours. The study also examines PVG-1 as a derivative of PVG. ResultsDistinct modifications in vocal fold vibrations across various stages (open, closed, closing, and opening) are documented using PVG, PVG-1, GVG, DKG, and PVG trajectory. Healthy vocal folds exhibit consistent periodic patterns, while disorders like leukoplakia, laryngitis, and papilloma manifest unique features due to structural and functional variations. The methods employed identify both structural irregularities and functional deviations in vocal fold vibrations. ConclusionsComparative analyses using PVG and other visualization techniques aids in understanding the glottal dynamics of voice disorders. While laryngoscopy and stroboscopy are valuable for real-time assessment, offline analysis techniques like PVG and GVG are useful for tracking glottal dynamics during phonation and assessing treatment effectiveness over time. Studies like this will contribute to standardizing the PVG based diagnostic criteria for its clinical utility, and development of classification tools for improved diagnosis and management of vocal fold pathologies.

AROA based Pre-trained Model of Convolutional Neural Network for Voice Pathology Detection and Classification

With the demand for better, more user-friendly HMIs, voice recognition systems have risen in prominence in recent years. The use of computer-assisted vocal pathology categorization tools allows for the accurate detection of voice pathology diseases. By using these methods, vocal disorders may be diagnosed early on and treated accordingly. An effective Deep Learning-based tool for feature extraction-based vocal pathology identification is the goal of this project. This research presents the results of using EfficientNet, a pre-trained Convolutional Neural Network (CNN), on a speech pathology dataset in order to achieve the highest possible classification accuracy. An Artificial Rabbit Optimization Algorithm (AROA)-tuned set of parameters complements the model's mobNet building elements, which include a linear stack of divisible convolution and max-pooling layers activated by Swish. In order to make the suggested approach applicable to a broad variety of voice disorder problems, this study also suggests a unique training method along with several training methodologies. One speech database, the Saarbrücken voice database (SVD), has been used to test the proposed technology. Using up to 96% accuracy, the experimental findings demonstrate that the suggested CNN approach is capable of detecting speech pathologies. The suggested method demonstrates great potential for use in real-world clinical settings, where it may provide accurate classifications in as little as three seconds and expedite automated diagnosis and treatment.

Zoom in: factors affecting vocal habits during online meetings, a prospective trial on 40 subjects.

To identify factors that influence vocal habits during online meetings (OMs). A prospective trial of forty participants without any known hearing or vocal cord disorders. Subjects participated in an OM divided into six randomly ordered sections, with alterations in audio/speaking equipment and language: the computer's speaker-microphone, a single earbud, two-earbuds or headphones; with/without video, native-language-speaking (Hebrew) versus second language-speaking (English). Each section included free speech, sustained phonation, and a standardized passage. Participants ranked their vocal-effort for each section. Three blinded raters independently scored the voice using the GRBAS scale, and acoustic analyses were performed. No significant difference in self-reported vocal effort was demonstrated between sections. Second-language speaking resulted in significantly increased intensity (p < 0.0001), frequency (p = 0.015), GRBAS (p = 0.008), and strain (p < 0.0001) scores. Using the computer's speaker/microphone resulted in significantly higher strain (p < 0.0001). Using headphones, single or two earbuds resulted in lower intensity and a lower strain score. No differences were detected between OMs with or without video. Using the computer's microphone/speaker or speaking in a second language during OMs, may result in vocal habits associated with vocal trauma.

The Relationship between Posture and Muscle Tensive Dysphonia in Teachers: A Systematic Scoping Review.

Teachers usually present work-related pain such as neck pain. Their posture could be the cause of these problems; indeed, it is often a sway-back posture. Furthermore, teachers can also experience problems with their voice such as dysphonia, specifically muscle tension dysphonia (MTD). This scoping review aims to find the correlation between teachers' posture and MTD. It also studies how a posture-based treatment can influence this disorder. Randomized controlled trials, controlled clinical trials, prospective cohort studies, and cross-sectional studies that considered the relationship between posture and MTD and that included teachers in their sample. The search led to an initial number of 396 articles; after the screening process, a final number of eight articles were included. A total of 303 patients were analyzed and all showed altered alignment of the head around the cervical spine with hypertonus of the cricothyroid, suprahyoid, and sternocleidomastoid muscles. Although MTD is a disorder with a multifactorial etiology, the articles revealed a correlation between posture and MTD related to a forward protraction of the cervical spine with a hypertonus of the laryngeal and hyoid musculature. This study also detected that an intervention in posture could reduce vocal disorders.

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson's Disease: A Study on Speaker Diarization and Classification Techniques.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King's College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones

Background: The integration of edge computing into smart healthcare systems requires the development of computationally efficient models and methodologies for monitoring and detecting patients’ healthcare statuses. In this context, mobile devices, such as smartphones, are increasingly employed for the purpose of aiding diagnosis, treatment, and monitoring. Notably, smartphones are widely pervasive and readily accessible to a significant portion of the population. These devices empower individuals to conveniently record and submit voice samples, thereby potentially facilitating the early detection of vocal irregularities or changes. This research focuses on the creation of diverse machine learning frameworks based on vocal samples captured by smartphones to distinguish between pathological and healthy voices. Methods: The investigation leverages the publicly available VOICED dataset, comprising 58 healthy voice samples and 150 samples from voices exhibiting pathological conditions, and machine learning techniques for the classification of healthy and diseased patients through the employment of Mel-frequency cepstral coefficients. Results: Through cross-validated two-class classification, the fine k-nearest neighbor exhibited the highest performance, achieving an accuracy rate of 98.3% in identifying healthy and pathological voices. Conclusions: This study holds promise for enabling smartphones to effectively identify vocal disorders, offering a multitude of advantages for both individuals and healthcare systems, encompassing heightened accessibility, early detection, and continuous monitoring.

Three Episodes of Postanesthesia Paradoxical Vocal Cord Motion Disorder in the Same Patient: A Case Report.

Postanesthesia paradoxical vocal cord motion disorder (PVCMD) is often benign. However, if not recognized, PVCMD can lead to unnecessary treatments. Our patient had 3 different surgeries over a period of 20 months. The first episode of PVCMD occurred after a shoulder surgery, and the patient was reintubated. PVCMD was correctly diagnosed and treated successfully with reassurance after an ulnar nerve decompression. The third episode of PVCMD occurred after a cervical fusion surgery. Prevertebral edema from surgery further compromised the airways. Our case demonstrates the challenges of identifying and managing perioperative PVCMD, especially when surgical complications confound the airway management.

Fadiga vocal de professores brasileiros da rede pública durante o ensino remoto: estudo multicêntrico

ABSTRACT Purpose To analyze teachers’ self-perception of vocal fatigue and its relationship with the amount of the speech and loudness, vocal disorders, mask usage, and working conditions. Methods A multicenter, exploratory, observational, analytical, and cross-sectional study of the web survey type during the period of emergency remote teaching. Convenience sampling was used, comprising 263 public school teachers from the cities of Belo Horizonte, Campinas, São Paulo and Salvador, working inEducation. The Google form was sent with sociodemographic, work and health questions, use of a face mask, in addition to self-assessment protocols and self-assessment Voice Disorder Screening Index (VDSI), Degree of Speech Quantity and Vocal Intensity (DSQVI), and Vocal Fatigue Index (VFI). Results Most of the teachers showed a suspected vocal disorder (55.9%) and perceived vocal fatigue (83.7%), with good voice recovery after rest (74.5%). There was a statistical association between self-perceived vocal fatigue and suspected voice disorder (OR: 25.64 / CI: 95% 7.60-86.45) and working in high school (OR: 0.35 / CI: 95%: 0.16-0.76). Conclusion Teachers reported a high prevalence of vocal fatigue, with satisfactory voice recovery while conducting online classes during the pandemic. The presence of vocal disorder increased the likelihood of self-perceived vocal fatigue symptoms, and working in high school reduced this likelihood.

Variability of Maximum Glottal Angle on Clinical Sniff Task Differs in Patients With Functional and Organic Laryngeal Pathologies Compared to Healthy Controls

ObjectivesPractitioners rely heavily on flexible endoscopic visualization of the true vocal folds during a repeated “sniff-ee” maneuver to assess vocal fold mobility. However, the human eye lacks the temporal and spatial precision required to accurately gauge fine differences in maximal glottal angle. This study compared differences in maximal glottal angle variables during “sniff-ee” maneuvers across patients with various voice and laryngeal breathing disorders. MethodsWe retrospectively measured glottal angle from flexible laryngoscopy exams in six groups of patients with voice and upper airway disorders: laryngeal dystonia/essential tremor (LD/ET), vocal fold lesions, vocal fold atrophy, paradoxical vocal fold motion disorder (PVFMD), muscle tension dysphonia (MTD), and healthy controls. Maximum glottal angle (GAMAX) and average glottal angle (GAAVG) were calculated during three serial “sniff-ee” maneuvers for all participants. Individual disorder groups (MTD, PVFMD, LD/ET, atrophy, lesion) and broader disorder types (functional and organic) were compared to healthy controls using simple linear regression analyses. ResultsNo significant difference in either GAMAX or GAAVG was found between controls and the disorder subgroups or broader disorder type (function and organic). However, there were statistically significant differences in the variability of GAMAX in both PVFMD (6.2° more variability; p<0.001) and LD/ET (5.8° more variability; p<0.001) compared to healthy controls. ConclusionPatients diagnosed with LD/ET and PVFMD both demonstrated significantly more variability in their GAMAX compared to healthy controls, suggesting that movement consistency or coordination may be relatively compromised in these patient groups. Further research is warranted to investigate the sensitivity and specificity of glottal angle variability in diagnosing PVFMD and LD in clinical or research settings. Level of Evidence4 Short SummaryLaryngeal exams from five patient groups were compared to those from healthy controls. Patients with paradoxical vocal fold motion disorder and laryngeal movement disorders exhibited significantly greater variability of glottal angle during sniff maneuver compared to healthy controls.