Anaïs Rameau

Pharyngolaryngeal vascular malformations (PLVMs) are commonly treated with sclerotherapy; however, its efficacy for localized protruding lesions is often limited and may require multiple sessions with a risk of airway edema. We describe an endoscope‐assisted transoral technique using flexible‐tip endoscopy and tailored laryngoscopic exposure for safe, function‐preserving resection. This technique was applied in 7 patients with PLVMs. Surgical exposure was achieved using an FK‐WO retractor or curved laryngoscope, and visualization was provided by a flexible‐tip rigid endoscope. Resection was performed along the submucosal plane while avoiding intralesional incision. All procedures were completed successfully with minimal blood loss and no postoperative complications. Oral intake was resumed within 3 postoperative days, and no recurrence was observed during follow‐up. Endoscope‐assisted transoral resection provides a safe and effective treatment for selected PLVMs. This approach allows precise lesion control while preserving function and may represent an alternative to repeated sclerotherapy in appropriately selected patients.

Background: Gender-affirming voice training (GAVT) has reduced gender incongruence for transgender and gender-nonconforming (TGNC) individuals, but remains limited by cost and scarcity of specialized providers. Mobile health (mHealth) is a bridge to these access gaps; yet, few high-quality, clinically informed mHealth apps exist within voice clinical sciences. Objective: We previously created a free mobile app (Attuned; Weill Cornell Medicine and Ithaca College) to deliver clinically informed GAVT through structured instructional videos narrated by a speech-language pathologist and a laryngologist, practice exercises, and pitch-tracking tools, developed with a community advisory board of TGNC individuals. This mixed methods study aimed to assess the app’s usability and users’ impressions. Methods: Adult TGNC individuals were recruited nationwide via word of mouth and advertising through lesbian, gay, bisexual, transgender, queer, or questioning (LGBTQ+) community organizations. Participants completed a baseline semistructured interview and demographics survey, used Attuned for one week, and completed a second interview regarding their impressions. Interviews were transcribed verbatim and coded inductively in MaxQDA (VERBI Software GmbH). Three authors independently generated codebooks that were reconciled via consensus, then 2 authors applied the finalized codebooks to the remaining transcripts (preconsensus agreement 95.7%; Cohen κ=0.86). Attuned was also evaluated by 2 reviewers experienced in mHealth evaluation and not involved in the app development, using the Mobile App Rating Scale (MARS), the Intercontinental Medical Statistics (IMS) Institute for Health Care Informatics Functionality Scores, the Centers for Disease Control (CDC) Modified Clear Communication Index, the Institute of Medicine (IOM) strategies for creating health-literate mHealth apps, the Patient Education Materials Assessment Tool (PEMAT), and the System Usability Scale (SUS). Readability of the didactic scripts was also assessed with 6 validated formulas. Results: Eighteen TGNC individuals completed the study (median age 27.5 years, age range 19‐50 years). Ten identified as women and/or transgender women, 6 as nonbinary or gender-nonconforming, and 2 as men or transgender men. Eight themes were identified. Participants identified voice as a crucial determinant of others’ perceptions of their gender, with voice-related dysphoria common. Cost and difficulty finding providers were primary barriers to GAVT, and existing free resources were viewed as unstructured and untrustworthy. Postuse impressions were largely positive, particularly regarding the structured curriculum, pitch tracker, convenience, and free access, though lack of individualized feedback was a key limitation. Mean scores were 4.21/5 (SD 0.53) for MARS; IMS functionality, 6.5/11 (SD 0.7); CDC index, 82.5% (SD 24.7%); IOM strategies, 28.5/33 (SD 2.1); and PEMAT understandability and actionability 100%, and SUS 92.5/100 (SD 10.6), with most readability formulas at or below an eighth-grade level. Conclusions: Voice is central to gender identity for TGNC individuals; yet, structured and affordable GAVT remains largely inaccessible. Attuned was positively received by this initial cohort and scored highly on usability, health literacy, and readability, though findings are limited by potential acquiescence bias and the raters’ authorship roles.

Background and aims: Vocal biomarker technology has emerged as a promising non-invasive health assessment tool, with artificial intelligence enabling the screening, diagnosis, and monitoring of conditions including Parkinson's disease, Alzheimer's disease, depression, and heart failure. Vocal biomarkers are defined as vocal features, alone or in combination, validated as indicators of clinical outcomes. Startups in this space are generating substantial investment and anticipatory discourse around future commercialization, while the scientific foundation and governance framework remain unestablished. Drawing on Sabina Leonelli's epistemic economy framework, which analyzes how data are produced, valued, and governed within social, institutional, and commercial arrangements, we examine how voice data are converted into entities with clinical and commercial value, and at what scientific and ethical cost. Methods: We performed a qualitative interview study using an interpretive framework. Virtual semi-structured interviews were conducted with representatives from eleven vocal biomarker startups identified through purposive sampling. Interviews addressed data collection methods, IRB compliance, informed consent, participant demographics, geographic sourcing, and financial incentives. Two researchers independently performed thematic analysis, reconciling differences via discussion. Results: Six themes were identified: (1) the absence of shared data standards introduced substantial hardware and protocol heterogeneity, uncontrolled confounding, and label noise, undermining scientific validity; (2) IRB engagement ranged from robust multi-site review to complete absence of oversight; (3) consent practices varied from granular opt-in to implied consent and minimal disclosures; (4) voice data were acknowledged as inherently non-anonymizable, creating unresolved privacy and re-identification risks; (5) data sourcing was asymmetric, with collection from low- and middle-income countries in 4 out of 11 startups while targeting commercial deployment in high-income countries; and (6) commercial incentives produced proprietary protocols that preclude independent validation and impede data sharing. Conclusion: The vocal biomarker startup ecosystem operates as an epistemic economy in which methodological, regulatory, and governance choices tend to favor commercial interests and high-income populations over scientific rigor and equity. Standardized data provenance requirements, reassessment of voice data's risk classification under existing privacy frameworks (e.g., HIPAA, GDPR), and participatory governance including source communities are urgently needed to ensure vocal biomarker products are scientifically valid, ethically grounded, and equitably developed.

Introduction: Although HRM is widely considered a well-tolerated and minimally invasive procedure, pain and anxiety are reported by patients. We aimed to investigate whether patient-reported and physiologic indicators of procedure-related pain and anxiety are associated with high-resolution manometry (HRM) findings at rest and during swallowing. Methods: HRM data and patient-reported measures and physiologic indicators of pain and anxiety were collected from 36 outpatient adults. Multivariable linear regression models were used to assess the associations between pain/anxiety and upper esophageal sphincter (UES)/lower esophageal sphincter pressures and esophageal motility outcomes. Predictor variables included (1) a validated visual analogue scale (VAS) of self-reported pain; (2) the State Trait Anxiety Inventory (STAI) sum score; (3) the Short-Form McGill Pain Questionnaire (SFMPQ); (4) heart rate before the procedure (HR); and (5) average slope of galvanic skin response (GSRslope). Relationships were assessed both at rest for basal measures and during swallowing for peristaltic measures. Results: Few statistically significant relationships were observed between patient-reported measures or physiologic indicators of anxiety/pain with esophageal or sphincter measures. However, UES mean residual pressure during swallowing was negatively associated with VAS pain ratings (p = 0.04) and the GSRslope (p = 0.01). Additionally, as SF-MPQ increased, the percentage of intact swallows decreased (adjusted R2 = 0.146; p = 0.012). Conclusion: UES resting pressure during HRM procedures was not significantly associated with patient-reported anxiety or pain. However, we saw a small statistically significant inverse association between UES mean residual pressure and patient-reported pain and physiologic arousal (GSRslope). Future research may benefit from continuous physiologic monitoring throughout HRM procedures and from inclusion of a larger sample with more diverse pathology.

Objectives: To trace the historical origins of the fiberoptic nasopharyngolaryngoscope (laryngoscope) and to shed light on the innovative scientists who developed this technology. Data Sources: Primary sources included original research articles and abstracts reporting the initial development of the fiberoptic laryngoscope and similar technologies, original documents from the University of Tokyo's Research Institute of Logopedics and Phoniatrics (RILP) Annual Bulletin, and an interview with the lead inventor. Review Methods: Available information was reviewed regarding: how and why the fiberoptic laryngoscope was developed, what technologies preceded this development, who the inventors were, what historical context this technology was shaped in, and its long‐term impact. Results: The fiberoptic laryngoscope was invented in 1967 by Drs. Masayuki Sawashima, Hajime Hirose, and Osamu Fujimura at the University of Tokyo's RILP. This groundbreaking innovation arose during a wave of rapid technological advancement in Japan following World War II. Dr. Sawashima highlights that a key inspiration for the fiberoptic laryngoscope was the success of the fiberoptic bronchoscope, developed by Japanese physician‐scientist Dr. Shigeto Ikeda in 1962. The inventors of the fiberoptic laryngoscope reported the first real‐time images and videos of the glottis during unperturbed speech and breathing, revolutionizing understanding of laryngeal function and setting the stage for modern laryngology and speech language pathology. According to Dr. Sawashima, the team never patented their technology. Conclusion: The flexible laryngoscope was invented in 1967 by Sawashima, Hirose, and Fujimura at the University of Tokyo RILP, a foundational contribution to modern laryngology that has gone largely unrecognized in English‐language literature. Level of Evidence: NA

Objective: To evaluate the perceptual realism of synthetic videostroboscopic laryngeal images and determine the relationship between training duration and clinician classification accuracy. Methods: Synthetic images were generated using StyleGAN3 from two age‐stratified datasets: Dataset A (≥ 65 years) and Dataset B (< 65 years). A total of 114 clinicians evaluated a randomized, balanced‐controlled 36‐image survey drawn from a 144‐image pool containing real frames and five StyleGAN3 training intervals (5120–25,000 kimg). Accuracy was analyzed across image classes, experience levels, and viewing devices. Results: Clinicians demonstrated significantly higher mean accuracy identifying true clinical frames than synthetic generations (70.9% vs. 57.8%, p < 0.001). Overall survey accuracy was 59.9% ± 14.2%, with realism peaking at 20,120 kimg (44.8% accuracy). Accuracy dropped significantly between 5120 kimg (79.6%) and 10,120 kimg (59.1%, p < 0.001), with diminishing returns thereafter. No significant accuracy difference existed between Datasets A and B ( p = 0.27). The effect of specialty reached borderline significance ( p = 0.059), though limited by severe subgroup imbalances (e.g., n = 5 fellows, n = 2 residents). Computer users (63.9%) were significantly more accurate than phone users (56.2%, p = 0.014). A confounder analysis confirmed specialty and device choice were statistically independent ( p = 0.164). Conclusion: StyleGAN3 produces synthetic laryngeal images demonstrating high perceptual realism. The significant real‐versus‐synthetic accuracy gap highlights that while true frames remain distinct, synthetic generations achieve profound ambiguity. Perceptual realism plateaus early (10,120–20,120 kimg), suggesting moderate training durations can produce human‐perceived realism while reducing computational and environmental demands. Level of Evidence: N/A.

Introduction: Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. The objective of this work is to develop universally accepted definitions in the rapidly evolving field of vocal biomarkers, as part of the VOCAL (Vocal Biomarker Guidelines for Ontology, Classification, Application, and Logistics) initiative, a structured, international consensus-based framework that aims to provide standards and guidelines. Methods: VOCAL is a rigorous, international, multi-stage consensus-building study conducted in 2024–2025. It is a multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), involving a group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, and ethics. VOCAL’s iterative process involved five rounds of review and feedback, and an in-person workshop at the 2025 Bridge2AI Voice Symposium. Results: Consensus-based definitions for vocal biomarkers were developed, spanning from broad concepts to domain-specific measures. A hierarchical continuum model of vocal biomarkers was established. We first distinguished between the concepts of vocal measures and vocal biomarkers. We then defined terms from broad, overarching concepts (Level 0: Biomarker, Digital Biomarker, Vocal Biomarker) to more specific physiological and cognitive domains (Level 1: Cardio-Respiratory Acoustic; Level 2: Voice; Level 3: Speech/Articulatory; Level 4: Cognitive/Language, including linguistic and paralinguistic subtypes). Conclusion: This work provides a shared vocabulary that is essential for fostering communication through interdisciplinary collaboration, improving the quality and efficiency of research and development, and ensuring the ethical, reliable, and scalable deployment of future voice-based health technologies. It lays foundational groundwork for upcoming guidelines and standards, which are crucial for advancing the field of vocal biomarkers into widespread clinical utility.