Search Results (514)

Background/Objectives: The integration of artificial intelligence (AI) and digital technologies into dental practice is reshaping clinical workflows, administrative processes, and, increasingly, the patient experience and the doctor–patient relationship. While prior research has documented the attitudes of clinicians and practice managers, the perspective of the patient remains comparatively underexplored. This study examined how dental patients perceive AI integration and digital tools across the dental care pathway, together with the associated implications for data security, cost, and the human dimension of care. Methods: A cross-sectional, questionnaire-based study was conducted among 200 dental patients in Bucharest, Romania, and the surrounding region. The instrument assessed perceived difficulty and availability regarding digital technology, current use of digital tools, demographic and educational characteristics (age, gender, practice environment, educational level), and two attitudinal dimensions, namely digital prudence and concern for technological sustainability, across five subdomains of the dental care pathway: scheduling, diagnosis, treatment planning, feedback, and follow-up (dispensarization). Responses were analyzed using non-parametric tests and exploratory principal component analysis with internal-consistency validation. Results: Patients expressed moderate-to-high interest in AI support during the diagnostic (median = 3.3, IQR = 2.7–3.9) and feedback (median = 3.11, IQR = 2.78–3.67) stages and the lowest interest in scheduling (median = 2.7, IQR = 2.0–3.3). A marked level of digital prudence was observed (median = 3.24, IQR = 2.82–3.61), reflecting concerns about data security, automation, and a possible weakening of the clinician–patient bond. Younger and academically educated patients reported lower perceived difficulty, higher availability, and greater current use of digital tools (all p ≤ 0.001); counterintuitively, the same patients scored significantly higher on digital prudence (Spearman’s ρ = −0.260, p < 0.001). Greater familiarity with digital tools was therefore accompanied by a more critical awareness of their informational risks rather than by uncritical acceptance. Conclusions: Dental patients approach AI through a dual lens of openness and informed caution, welcoming efficiency gains in the clinical and continuity-of-care stages while voicing measured concerns about data security, affordability, and the preservation of human contact. To interpret this profile, we propose two conceptual contributions: a mapping of patient needs onto Maslow’s hierarchy in the context of AI-mediated care and the Informational VUCA framework, which characterizes the volatility, uncertainty, complexity, and ambiguity that patients face when navigating AI-generated information. The findings point to a clear practical agenda of transparent communication, robust data governance, and education strategies adapted to patients’ educational and demographic profiles, so that AI-enhanced workflows strengthen rather than erode the doctor–patient relationship. Full article

Background: Parkinson’s disease (PD) is a chronic, slowly progressive, and irreversible neuropathological disorder characterized by the progressive degeneration of specific neurons responsible for producing neurotransmitters essential for motor control. Although PD primarily affects motor function, various non-motor symptoms commonly emerge during the prodromal phase. These include autonomic dysfunction, cognitive and neurobehavioral disorders, and sensory and sleep abnormalities. Notably, speech and voice alterations, particularly hypokinetic dysarthria, are frequent manifestations. This research presents a methodology to distinguish between individuals with PD and healthy controls using voice signals through speech recognition and machine learning (ML) techniques. A dataset comprising 81 voice samples (41 healthy controls and 40 PD patients) was utilized to extract two types of cepstral features: Mel-frequency cepstral coefficients (MFCCs) and subband-based cepstral coefficients (SBCs). These extracted features were used to train and evaluate three ML algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). Results: Among the algorithms evaluated, the SVM-SBC model exhibited the highest performance, achieving an accuracy of 79%, a sensitivity of 75.5%, and an Area Under the ROC Curve (AUC-ROC) of 84%. Conclusions: This study highlights the potential of integrating cepstral features with machine learning algorithms to develop reliable, non-invasive tools for the early detection of PD. Full article

Globally, the World Health Organization (WHO) estimates that there are 20.6 million new cancer cases each year, and its incidence is expected to rise as the global population ages. At the same time, new technologies and advanced treatments are increasing survival rates, particularly in high-income countries. The need to understand cancer’s impact on psychosocial well-being and patients’ existential lifeworld is reflected in research, literature, pathographies, and other art forms. This article examines blogs as a distinctive arena for meaning-making, healing, and the articulation of cancer experiences. Through a close reading of three cancer blogs written by individuals with pelvic and colorectal cancers living in Norway, we examine illness narratives that often receive limited attention in private and public discourse. Drawing on Paul Ricoeur’s theory of narrative identity and configuration, we analyse how bloggers organise events, create meaning, and narrate their lives during potentially terminal illness. We also engage with Mikhail Bakhtin’s reflections on genre to explore blogs’ communicative character, heterogeneity of voices, and orientation towards anticipated responses. The findings suggest that cancer blogs function as intentional narrative and social resources during profound existential transformation. They also demonstrate that philosophical and literary concepts are valuable tools for analysing blogs as health humanities texts capable of capturing chaotic and disrupted illness stories. Full article

Laryngeal pathology requires individualized management because the larynx integrates airway protection, phonation, swallowing, and respiratory function within a compact and highly variable anatomical framework. This narrative review examines how laryngeal anatomy and morphometry support interdisciplinary collaboration and personalized care in oncologic, stenotic, functional, and reconstructive laryngeal disease. Evidence from morphometric studies, CT, MRI, endoscopy, ultrasonography, three-dimensional reconstruction, artificial intelligence, and multidisciplinary clinical workflows was synthesized qualitatively. Key parameters—including vocal fold length, glottic width, thyroid cartilage angle, cricoid diameter, subglottic diameter, anterior commissure thickness, and the status of paraglottic and pre-epiglottic spaces—provide actionable information for diagnosis, T-staging, airway assessment, surgical planning, reconstruction, and functional rehabilitation. Morphometry concretely informs clinical decisions: thyroid cartilage angle guides thyroplasty and phonosurgical planning; subglottic diameter supports stenosis surgery and airway instrumentation; and anterior commissure, conus elasticus, cartilage, and deep-space measurements refine oncologic staging and margin strategy. Technological accelerators, including AI segmentation, radiomics, 3D printing, photogrammetry, and ultrasonography, extend morphometry from static measurement toward predictive modeling and patient-specific simulation. However, implementation remains limited by heterogeneous CT protocols, inconsistent measurement planes, uneven access to advanced technologies, lack of global normative databases, and unresolved ethical issues surrounding AI validation and data governance. This review supports standardizing CT morphometry using parallel vocal fold planes, routinely measuring anterior commissure thickness in T1 glottic cancer, incorporating ultrasonography as a first-line morphometric tool in voice clinics, and validating AI segmentation against population-specific morphometric norms. Laryngeal morphometry should therefore become a routine decision-making framework for precision laryngology. Full article

Vocal rise time (VRT) acoustically measures from when vocal cord vibration begins to when phonation achieves a peak steady state. This acoustic-based study examines manual and automatic VRT measurements based on different vowel onset recordings (e.g., hard, modal, soft) from four datasets containing 146 adult Australian English-speaking females. Experiments test how the automatic Voice Onset Analysis Tool (VOAT) settings impact VRT measurement estimations. When compared to VOAT VRT baseline setting results, parameter adjustments using a Hilbert envelope 400 ms analysis segment with a low smoothing factor decreased VRT mean absolute error as low as 15.5 ms and 21.3 ms for modal and soft onsets, respectively. While the automated VOAT demonstrates objective repeatability with accelerated VRT extraction time advantages, still experiments herein show that the proposed adjusted settings are least accurate for hard onsets, whereby a mean absolute error as high as 71.0 ms is reported. Given three to five voice therapy sessions, females with voice disorders exhibited an 11% increase in VOAT VRT durations with reduced variation (6%), providing preliminary clinical evidence that this measure can assist in monitoring patients’ progress. This study reveals cross-corpora VRT norms per voice onset type, discusses VOAT parameter effects, and supports future automated VRT algorithm considerations. Full article

Background/Objectives: Endotracheal intubation can cause postoperative voice disturbances, even after short procedures. These effects have been documented in head and neck surgery, but direct comparisons between head/neck and abdominal surgeries are lacking. We sought to compare short-term postoperative voice changes in patients undergoing head/neck and abdominal surgery and to evaluate their association with intubation parameters and patient factors. Methods: We prospectively studied 80 adults (mean age X ± SD) undergoing either head–neck surgery (parotidectomy or total thyroidectomy) or abdominal surgery. Voice acoustic parameters (fundamental frequency (F₀), jitter, shimmer, intensity, maximum phonation time (MFT)) were measured preoperatively and on postoperative days 2, 14, and 30. Anesthetic parameters were recorded during the perioperative period. Anesthesiologists were blinded to the ongoing investigation. We used repeated-measures ANCOVA (with sex as a covariate) to test for group-by-time interactions and main effects. Effect sizes were calculated. Results: Eighty patients were included. All acoustic parameters showed significant postoperative changes (p < 0.001). The most pronounced alterations occurred in the early postoperative period, with decreased F₀, intensity, and MFT, and increased jitter and shimmer. Significant interaction effects between time and type of surgery were observed for F₀ (ηp² = 0.445), shimmer (ηp² = 0.210), intensity (ηp² = 0.267), and MFT (ηp² = 0.149), indicating distinct recovery patterns across groups. Patients undergoing abdominal surgery showed less pronounced and more rapidly resolving changes, whereas head and neck surgery was associated with greater and more persistent impairment. Among all parameters, shimmer demonstrated the largest overall effect size (ηp² = 0.338), suggesting high sensitivity to short-term voice changes. Perioperative factors, including duration of surgery, endotracheal tube size, and BMI, were significantly associated with the extent of voice changes. Conclusions: Short-duration endotracheal intubation can lead to measurable short-term voice changes, which are more pronounced after head/neck surgery (particularly total thyroidectomy). Objective acoustic analysis detected these subclinical alterations even when patients were asymptomatic. These findings underscore the importance of monitoring voice outcomes and optimizing intubation techniques, although future work is needed to correlate acoustic changes with laryngeal exam findings. Full article

Introduction: Voice-based digital biomarkers have emerged as a promising approach for distinguishing individuals with neurodegenerative disorders, particularly Parkinson’s disease (PD), from healthy subjects (HS). With the increasing availability of smartphone and web-based recording tools, voice data can be collected efficiently in both clinical and remote settings. However, further validation is required before such approaches can be translated into routine clinical practice. Methods: This study used a cross-sectional analysis at the recording level, treating repeated recordings from the same participant as separate observations collected at Vito Fazzi Hospital in Lecce, Italy. Speech recordings from individuals with Parkinson’s disease (PD) and healthy controls were collected using the dedicated Talia smartphone and web application. Sustained vowel phonation (/a/) was analyzed as the primary speech task. Following data acquisition, feature extraction was performed as a crucial step in the speech analysis pipeline, as the quality and relevance of the extracted features directly influence the ability of machine learning models to discriminate between Parkinson’s disease (PD) patients and healthy controls. To capture various aspects of speech impairment associated with PD, a comprehensive set of acoustic features was extracted, including long-term features (pitch, jitter, and shimmer), nonlinear descriptors such as Recurrence Period Density Entropy (RPDE), and short-term feature based on Mel-Frequency Cepstral Coefficients (MFCCs). These features were subsequently used to develop and evaluate machine learning models for the classification of Parkinson’s disease and healthy subjects. Feature selection was performed using SHAP to identify the most informative vocal biomarkers. Model performance was assessed using five independent random train–test splits (70% training and 30% testing), supported by an internal five-fold cross-validation procedure within the training data. Multiple machine learning models were developed and evaluated, including Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, Decision Tree, Artificial Neural Network, and Gradient Boosting. Results: The evaluated models demonstrated strong recording-level classification performance. Artificial Neural Networks (ANN) and K-Nearest Neighbors (KNN) achieved the highest accuracy scores (0.9545 and 0.9494, respectively), along with superior recall (up to 0.9500), precision (up to 0.9551), and F1-score (up to 0.9525). Both models also exhibited excellent discriminative ability, with ROC-AUC values reaching 0.9882 (ANN) and 0.9893 (KNN). In contrast, Naive Bayes and Decision Tree showed comparatively lower performance across all metrics. Log-loss analysis further confirmed the robustness of ANN and KNN, which achieved the lowest values (0.2552 and 0.2510, respectively), indicating well-calibrated predictions. Overall, the findings highlight the consistency and generalizability of ANN and KNN across cross-validation splits. Conclusions: This study demonstrates that machine learning models, particularly ANN and KNN, can effectively differentiate Parkinson’s disease from healthy conditions using voice recordings. The integration of explainable AI for feature selection enhances model transparency and clinical relevance. However, the reported performance estimates were obtained from a recording-level analysis and should be interpreted as preliminary findings. Further studies involving larger cohorts and participant-level validation strategies are required to determine the generalizability and clinical applicability of these approaches. Full article

Background/Objectives: AI voice interpretation applications like Google Translate in speech/voice mode are increasingly used in clinical settings to address verbal language access challenges, yet evidence comparing their performance to qualified in-person medical interpreters in authentic clinical encounters remains limited. The aim of this study is to compare the linguistic and clinical accuracy of AI-based Google Translate in speech/voice mode and qualified in-person medical interpretation using spoken real-world speech segments from clinical encounters. Methods: Outpatient clinical encounters involving patients with limited English proficiency were audio-recorded. Fourteen physician speech segments (mean length 78.5 words) representing common diagnostic, treatment, and counseling content were extracted, spoken in English into Google Translate speech/voice mode, and translated into six languages. Audio recordings of the same sentence segments were provided to qualified in-person medical interpreters. All non-English translations were back-translated into English by professional interpreters. Researchers and a clinician evaluated the back-translated English speech segments for linguistic accuracy, completeness, and clinical significance. Qualitative analyses examined error patterns and contextual loss; quantitative comparisons assessed error rate differences across languages and interpretation conditions. Results: Google Translate in speech/voice mode exhibited significantly higher linguistic errors (χ²[1] = 19.78, p < 0.001) and clinical accuracy errors (χ²[1] = 45.07, p < 0.001) than qualified medical interpreter translations. Clinically significant error rates were 33.3% for Google Translate in speech/voice mode versus 4.8% for interpreter-generated translations. Error rates were also higher for less commonly spoken languages compared to commonly spoken languages when using Google Translate in speech/voice mode (42.9% vs. 14.3%). Conclusions: Qualified in-person interpreters provided translated speech segments that had fewer errors that were either linguistically or clinically significant, and remain essential for safe, accurate clinical communication. Full article

Background/Objectives: Indwelling tracheoesophageal prostheses have a limited lifespan and require repeated replacement. We examined the determinants of the replacement interval. Methods: This study conducted a retrospective single-center analysis of 147 consecutive Provox replacements in 82 laryngectomized patients (June 2024–February 2026), reported according to STROBE, using mixed-effects and gap-time recurrent-event models. Results: Median device survival was 141 days. Prior irradiation was associated with a shorter interval (−38.3 days; 95% CI −67.2 to −9.3; p = 0.010), with a gradient by treatment intensity: the median survival was 179, 136, and 84 days after primary surgery, radiotherapy alone, and chemoradiotherapy (log-rank p < 0.001; cluster-robust hazard ratios 2.19 and 5.13 against primary surgery). A higher cumulative replacement burden—in terms of lifetime exchanges per patient, with a median of eight and range of 1–28—was also associated with shorter intervals (β = −3.70 days per additional lifetime exchange; 95% CI −5.84 to −1.57), but it summarizes the same event process as the outcome and is reported as a phenotype marker. Intervals ending in elective exchange were longer (β = 35.47 days; 95% CI 15.80 to 55.14), but in the primary recurrent-event model, containing only covariates fixed at interval start and retaining censored intervals, elective placement showed no association with device lifetime (HR 0.574; 95% CI 0.309–1.066; p = 0.079); between-patient heterogeneity remained substantial (θ = 0.452; p = 0.002). Complications occurred in seven of 147 procedures (4.8%), which were all managed without life-threatening events. Conclusions: Prior oncological treatment, particularly chemoradiotherapy, is associated with materially shorter device survival and can be used to identify patients likely to require more frequent exchange. Outpatient exchange appears safe. The association between elective replacement and longer intervals reflects conditioning inherent in comparing scheduled with failure-driven exchange, not a benefit of scheduling. Full article

This paper analyzes contemporary medicine as a prime arena for the expression of “engaged spirituality”, understood as an action-oriented approach aimed at social and clinical change. The focus is on palliative care, a person-centered model that challenges biomedical reductionism by integrating the physical, psychological, and spiritual dimensions of the patient. Through a review of the state of the art, the article reconstructs the historical separation between “health” and “salvation”, highlighting the critical issues of the Italian context: “institutional inattention” and difficulties in managing religious pluralism. In contrast, international experiences and the adoption of technical tools demonstrate that spiritual care is now measurable and essential clinical competency. The analysis presents the results of empirical research which, through the voices of healthcare professionals, shows how palliative care translates spirituality into operational practice. By valuing the concept of “total pain”, this approach not only improves quality of life at the end of life but also serves as a holistic paradigm exportable to other medical fields, ensuring the recognition of the person’s integral dignity. Full article

Background and Objectives: High-speed digital imaging (HSDI) is the most advanced method of phonation process analysis. Hoarseness is the most common indication for HSDI, and laryngopharyngeal reflux (LPR) is an etiological cofactor of hoarseness in 50% of cases. Clinical practice in HSDI includes the elective use of topical anesthesia (TA), but without defined recommendations and indications for use, and a potential altering effect of TA on the phonation process has not been unequivocally proven. The aim of this prospective, randomized, double-blind, placebo-controlled study was to examine the effect of TA application on the phonation process by analyzing Voice-Vibratory Assessment with Laryngeal Imaging (VALI) form parameters in subjects with LPR. Materials and Methods: A total of 70 volunteer subjects were included in the study; 50 subjects who completed both recording sessions were included in the final analysis. All subjects were recorded in two modalities, with TA application (lidocaine spray 100 mg/mL) and without it, on two consecutive days. Results: By evaluating phonation process parameters according to the VALI form, we determined that TA application during HSDI in subjects with LPR was not associated with statistically significant changes in the evaluated phonation process parameters. Conclusions: TA application during HSDI in subjects with LPR was not associated with significant changes in the analyzed phonation process parameters. Further research is needed to determine the benefit of TA in reducing pain and discomfort during examination, weighed against the risk of potentially life-threatening side effects, in order to individualize the approach to each patient. Full article

Background/Objectives: Suicide remains a major public health concern, with over 700,000 deaths annually worldwide. Current risk assessment is limited by the tendency of at-risk patients to deny suicidal ideation, with denial rates of approximately 50% among ideators and up to 78% among inpatient suicide decedents. Methods: This proof-of-concept study investigated whether deep learning applied to patient speech could distinguish psychiatric inpatients admitted following a recent suicide attempt from patients with severe depression without documented suicidal ideation. A multi-scale two-dimensional convolutional neural network with residual connections and spatial attention (Multi-Scale CNN) was trained on mel-spectrograms generated exclusively from patient speech extracted by automatic speaker diarization followed by manual verification. The dataset comprised recordings from 88 psychiatric inpatients (59 suicide attempters and 29 patients with severe depression without suicidal ideation). Model performance was evaluated using repeated patient-level stratified five-fold cross-validation, ensuring complete separation of participants between folds. Additional female-only, exploratory male-only, and repeated sex-matched analyses were performed to evaluate the potential influence of sex imbalance. Results: Repeated patient-level five-fold cross-validation yielded a mean classification accuracy of 72.7 ± 6.4% with a mean ROC-AUC of 0.824 ± 0.054, a sensitivity of 88.3 ± 9.5%, and a specificity of 41.3 ± 25.0%. Female-only analysis maintained good discriminative performance (ROC-AUC 0.866 ± 0.123), whereas repeated sex-matched analyses produced lower performance (ROC-AUC 0.621 ± 0.156), indicating that sex imbalance contributed to, but did not fully explain, the observed discrimination. The male-only analysis was considered exploratory because of the limited number of male patients in the depression group. Conclusions: These findings support the feasibility of voice-based patient-level analysis as a complementary objective approach for suicide risk assessment. Although the study remains exploratory because of the limited sample size and the absence of external validation, the revised validation strategy and sex-controlled analyses provide a substantially more robust estimate of model performance. Further multicenter prospective studies are required before clinical implementation. Full article

Background: Parkinson’s Disease (PD) is a neurodegenerative disorder frequently accompanied by speech impairments, which could serve as non-invasive biomarkers for early detection. This study investigates the efficacy of machine learning models trained on voice and speech acoustic features for distinguishing PD patients from healthy controls (HC) using the publicly available Italian Parkinson’s Voice and Speech (IPVS) dataset. Methods: A comprehensive set of acoustic features was extracted, including perturbation, prosodic and temporal features, Mel-Frequency Cepstral Coefficients (MFCCs), and Gammatone Cepstral Coefficients (GTCCs). These features were evaluated individually and in combination using six supervised classifiers: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), and Multi-Layer Perceptron (MLP). Results: The best-performing configuration combination of GTCC and acoustic features with the SVM model achieved 94.68% accuracy, 94.37% sensitivity, 95.04% specificity, 95.71% precision, 96.04% F1-score, an MCC value of 0.89, and an ROC-AUC of 0.98. In the combination of all feature sets, the most impressive performance was observed with the MLP classifier. This achieved 93.08% test accuracy, 94.30% sensitivity, 91.18% specificity, 94.30% precision, 94.30 F1-score, an ROC-AUC of 0.97, and an MCC value of 0.85. Conclusions: The findings demonstrate that combining clinically relevant acoustic features with robust machine learning classifiers offers a reliable, interpretable, and computationally efficient solution for PD detection. The use of a publicly available dataset, open-source tools, and subject-wise validation contributes to the reproducibility and clinical relevance of the proposed approach. This study reinforces the potential of speech as a digital biomarker for early PD detection and supports the integration of voice-based assessments into diagnostic platforms. Full article

Cancer patients receiving systemic therapy experience substantial treatment-related symptoms. Nurse-led digital health interventions, e.g., interactive voice response systems, web platforms, mobile apps, and telehealth, have emerged as strategies to strengthen supportive care. To evaluate its effectiveness, this systematic review summarizes evidence exclusively from randomized controlled trials (RCTs). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, four databases were searched from inception to January 2025 for eligible RCTs involving adults undergoing anticancer therapy; evaluating nurse-led or nurse-co-led interventions using digital or telecommunication technologies; reporting validated symptom or health-related quality of life (HRQoL) outcomes. Risk of bias was assessed. Nine RCTs (N = 3344) met criteria; seven had low risk of bias. Interventions using telephone systems, web portals, mobile apps, or videoconferencing reduced symptom burden and improved HRQoL. The Symptom Care at Home system reduced symptom burden by ~43%, with greatest effects from combined automated monitoring and nurse practitioner follow-up. Additional benefits included improved anxiety, self-efficacy, patient participation, fewer severe toxicities and hospitalization days. In conclusion, nurse-led digital interventions effectively reduce symptom burden and support HRQoL during systemic therapy. Multicomponent models integrating automated monitoring with structured nursing follow-up and decision support appear most beneficial. Full article

Objective: To evaluate the clinical outcome, safety and efficacy of the APrevent Vocal Implant System (VOIS) in patients with unilateral vocal fold paralysis (UVFP), with particular emphasis on the timing and impact of postoperative saline adjustments. Methods: This retrospective−prospective longitudinal study included 11 patients with chronic UVFP who underwent VOIS medialization thyroplasty (MT) under local anesthesia (n = 2) and general anesthesia (n = 9). Multidimensional voice parameters were analyzed preoperatively and at 1, 3, 6, and 12 months postoperatively. Statistical analyses included the Friedman test for repeated measures and the comparison of outcomes between pre- and each postoperative timepoints was evaluated with the Wilcoxon signed-rank test. Results: Significant and sustained improvements were observed across all multidimensional voice parameters. Mean mVHI-10 decreased from 31.7 ± 4.5 preoperatively to 5.8 ± 5.1 at 12 months, while mean MPT increased from 7.1 ± 3.8 to 14.4 ± 4.5 s (p < 0.05, r > 0.7). Acoustic parameters, including jitter, shimmer, and NHR, demonstrated progressive improvement over 12 months. A high proportion of patients (72.73%) underwent postoperative saline adjustment at a mean interval of 6.23 ± 1.23 months, beyond the early postoperative edema phase, with each adjustment yielding further enhancement in voice outcomes. No major complications, including airway obstruction or hematoma, were observed. Conclusions: VOIS MT is safe and effective, providing sustained improvements in multidimensional voice outcomes. The ability to perform postoperative saline adjustments enables dynamic optimization of glottal closure, reducing the need for revision surgery and addressing evolving laryngeal biomechanics. These findings support VOIS as a flexible, adjustable alternative to static medialization techniques and provide dynamic voice optimization in patients with UVFP. Full article