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  1. Article ; Online: Voice as an AI Biomarker of Health-Introducing Audiomics.

    Bensoussan, Yaël / Elemento, Olivier / Rameau, Anaïs

    JAMA otolaryngology-- head & neck surgery

    2024  Volume 150, Issue 4, Page(s) 283–284

    MeSH term(s) Humans ; Voice
    Language English
    Publishing date 2024-02-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2701825-8
    ISSN 2168-619X ; 2168-6181
    ISSN (online) 2168-619X
    ISSN 2168-6181
    DOI 10.1001/jamaoto.2023.4807
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  2. Article ; Online: Pilot study for a novel and personalized voice restoration device for patients with laryngectomy.

    Rameau, Anaïs

    Head & neck

    2019  Volume 42, Issue 5, Page(s) 839–845

    Abstract: Background: The main modalities for voice restoration after laryngectomy are the electrolarynx, and the tracheoesophageal puncture [Correction added on 30 January 2020 after first online publication: The preceding sentence has been revised. It ... ...

    Abstract Background: The main modalities for voice restoration after laryngectomy are the electrolarynx, and the tracheoesophageal puncture [Correction added on 30 January 2020 after first online publication: The preceding sentence has been revised. It originally read "The main modalities for voice restoration after laryngectomy are the electrolarynx and the tracheoesophageal puncture."]. All have limitations and new technologies may offer innovative alternatives via silent speech.
    Objective: To describe a novel and personalized method of voice restoration using machine learning applied to electromyographic signal from articulatory muscles for the recognition of silent speech in a patient with total laryngectomy.
    Methods: Surface electromyographic (sEMG) signals of articulatory muscles were recorded from the face and neck of a patient with total laryngectomy who was articulating words silently. These sEMG signals were then used for automatic speech recognition via machine learning. Sensor placement was tailored to the patient's unique anatomy, following radiation and surgery. A personalized wearable mask covering the sensors was designed using 3D scanning and 3D printing.
    Results: Using seven sEMG sensors on the patient's face and neck and two grounding electrodes, we recorded EMG data while he was mouthing "Tedd" and "Ed." With data from 75 utterances for each of these words, we discriminated the sEMG signal with 86.4% accuracy using an XGBoost machine-learning model.
    Conclusions: This pilot study demonstrates the feasibility of sEMG-based alaryngeal speech recognition, using tailored sensor placement and a personalized wearable device. Further refinement of this approach could allow translation of silently articulated speech into a synthesized voiced speech via portable devices.
    MeSH term(s) Humans ; Laryngeal Neoplasms/surgery ; Laryngectomy ; Larynx, Artificial ; Male ; Pilot Projects ; Speech, Alaryngeal ; Voice
    Keywords covid19
    Language English
    Publishing date 2019-12-26
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 645165-2
    ISSN 1097-0347 ; 0148-6403 ; 1043-3074
    ISSN (online) 1097-0347
    ISSN 0148-6403 ; 1043-3074
    DOI 10.1002/hed.26057
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  3. Article ; Online: Standardizing Machine Learning Manuscript Reporting in Otolaryngology-Head & Neck Surgery.

    Crowson, Matthew G / Rameau, Anaïs

    The Laryngoscope

    2022  Volume 132, Issue 9, Page(s) 1698–1700

    MeSH term(s) Head/surgery ; Humans ; Machine Learning ; Neck/surgery ; Nose ; Otolaryngology
    Language English
    Publishing date 2022-06-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80180-x
    ISSN 1531-4995 ; 0023-852X
    ISSN (online) 1531-4995
    ISSN 0023-852X
    DOI 10.1002/lary.30264
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  4. Article ; Online: Access to Otolaryngologic Telemedicine Care Across the COVID-19 Pandemic at an Urban Tertiary Hospital System.

    Srinivasan, Yashes / Andreadis, Katerina / Ballakur, Sarita S / Rameau, Anaïs

    Ear, nose, & throat journal

    2024  , Page(s) 1455613241240560

    Abstract: Objective: ...

    Abstract Objective:
    Language English
    Publishing date 2024-03-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 750153-5
    ISSN 1942-7522 ; 0145-5613
    ISSN (online) 1942-7522
    ISSN 0145-5613
    DOI 10.1177/01455613241240560
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  5. Article ; Online: An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review.

    Alter, Isaac L / Chan, Karly / Lechien, Jérome / Rameau, Anaïs

    European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

    2024  Volume 281, Issue 5, Page(s) 2723–2731

    MeSH term(s) Humans ; Artificial Intelligence ; Otolaryngologists ; Machine Learning ; Otolaryngology ; Surgeons
    Language English
    Publishing date 2024-02-23
    Publishing country Germany
    Document type Review ; Journal Article
    ZDB-ID 1017359-6
    ISSN 1434-4726 ; 0937-4477
    ISSN (online) 1434-4726
    ISSN 0937-4477
    DOI 10.1007/s00405-024-08512-4
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  6. Article ; Online: 4290 Acoustic screening for the “wet voice” in a canine laryngeal model

    Anais Rameau

    Journal of Clinical and Translational Science, Vol 4, Pp 91-

    2020  Volume 92

    Abstract: OBJECTIVES/GOALS: Early dysphagia detection reduces risk of pulmonary complications, length of hospital stay, and overall healthcare costs. The biggest limitation for early detection has been the lack of a sensitive, reliable, and noninvasive screening ... ...

    Abstract OBJECTIVES/GOALS: Early dysphagia detection reduces risk of pulmonary complications, length of hospital stay, and overall healthcare costs. The biggest limitation for early detection has been the lack of a sensitive, reliable, and noninvasive screening tool. The bedside swallow examination may miss silent aspiration in up to 40% of patients. The objective of this study is to evaluate if acoustic parameters can distinguish normal and wet voice in a canine laryngeal model. Ultimately, our goal is to establish whether the sensitivity of the bedside swallow examination can be augmented with the addition of an acoustic screen in humans. METHODS/STUDY POPULATION: Two excised canine larynges were used for laryngeal phonation simulations under six different conditions over 48 hours. Acoustic recordings were obtained while the larynges were in vibration at the phonation threshold pressure. Phonation was recorded under dry conditions and when the laryngeal introitius was covered with low viscosity glycerin (9.5cP) or high viscosity glycerin (950cP), as well as in adducted and abducted conditions. The latter mimics glottic insufficiency seen in presbylarynx or vocal fold paralysis. RESULTS/ANTICIPATED RESULTS: A total of 112 voice samples were generated and analyzed for pitch, sound pressure level (SPL), % shimmer, % jitter, relative average perturbation (RAP), and noise-to-harmonics using PRAAT software. A multivariate regression model showed that pitch, SPL, % shimmer, % jitter and RAP could significantly predict wetness in abducted conditions only. Could you please add numbers and p values? DISCUSSION/SIGNIFICANCE OF IMPACT: This pilot study indicates that classic acoustic perturbation measures distinguish the dry from the wet larynx only in glottic insufficiency condition in an ex vivo canine laryngeal model. Our next step is to study whether non-linear time series analysis and machine learning can differentiate dry and wet phonation in both adducted and abducted conditions in our animal model. CONFLICT OF INTEREST ...
    Keywords Medicine ; R
    Subject code 410
    Language English
    Publishing date 2020-06-01T00:00:00Z
    Publisher Cambridge University Press
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Artificial Intelligence Governance and Otolaryngology-Head and Neck Surgery.

    Nwosu, Obinna I / Crowson, Matthew G / Rameau, Anaïs

    The Laryngoscope

    2023  Volume 133, Issue 11, Page(s) 2868–2870

    MeSH term(s) Humans ; Artificial Intelligence ; Otolaryngology ; Otorhinolaryngologic Surgical Procedures
    Language English
    Publishing date 2023-09-02
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 80180-x
    ISSN 1531-4995 ; 0023-852X
    ISSN (online) 1531-4995
    ISSN 0023-852X
    DOI 10.1002/lary.31013
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  8. Article ; Online: In Response to Concurrent Validity of the IOPI and Tongueometer Orofacial Strength Measurement Devices.

    Curtis, James A / Mocchetti, Valentina / Rameau, Anaïs

    The Laryngoscope

    2023  Volume 134, Issue 1, Page(s) E4

    MeSH term(s) Humans ; Deglutition Disorders ; Deglutition/physiology
    Language English
    Publishing date 2023-10-31
    Publishing country United States
    Document type Letter
    ZDB-ID 80180-x
    ISSN 1531-4995 ; 0023-852X
    ISSN (online) 1531-4995
    ISSN 0023-852X
    DOI 10.1002/lary.31099
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  9. Article ; Online: Concurrent Validity of the IOPI and Tongueometer Orofacial Strength Measurement Devices.

    Curtis, James A / Mocchetti, Valentina / Rameau, Anaïs

    The Laryngoscope

    2023  Volume 133, Issue 11, Page(s) 3123–3131

    Abstract: Objective(s): This study examined the concurrent validity of two orofacial strength manometers: (1) the Iowa Oral Performance Instrument (IOPI) - the current, gold standard orofacial manometer; and (2) the Tongueometer - a newly-available, lower cost, ... ...

    Abstract Objective(s): This study examined the concurrent validity of two orofacial strength manometers: (1) the Iowa Oral Performance Instrument (IOPI) - the current, gold standard orofacial manometer; and (2) the Tongueometer - a newly-available, lower cost, orofacial manometer.
    Methods: This study compared IOPI and Tongueometer pressure readings across three experimental conditions. Experiment 1 compared full setup (manometer + tongue bulb) pressure readings between the IOPI and Tongueometer. Experiment 2 compared IOPI tongue bulb and Tongueometer tongue bulb pressure readings, while controlling for manometer. Experiment 3 compared IOPI manometer and Tongueometer manometer pressure readings, while controlling for tongue bulb. Pressures were applied manually within a laboratory setting. Lin's concordance correlation (ρ
    Results: 539 trials were analyzed. There was a median absolute difference of 2.4 kPa in pressure readings between the IOPI and Tongueometer full setups (manometer + tongue bulb). Correlations revealed substantial agreement between IOPI and Tongueometer full setups (experiment 1: n = 292; ρ
    Conclusions: Differences in pressures were consistently observed between the Tongueometer and IOPI. Despite these differences, substantial agreement was present. These data suggest the Tongueometer may be a valid, lower cost alternative to the IOPI for objectively assessing orofacial strength in clinical practice.
    Level of evidence: Level 2 Laryngoscope, 133:3123-3131, 2023.
    MeSH term(s) Humans ; Female ; Deglutition ; Muscle Strength ; Tongue ; Breast Neoplasms ; Iowa
    Language English
    Publishing date 2023-05-30
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 80180-x
    ISSN 1531-4995 ; 0023-852X
    ISSN (online) 1531-4995
    ISSN 0023-852X
    DOI 10.1002/lary.30782
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  10. Article ; Online: A Novel Low-Cost, Open-Source, Three-Dimensionally Printed Thyroplasty Simulator.

    Kostas, Julianna C / Lee, Mark / Rameau, Anaïs

    Journal of voice : official journal of the Voice Foundation

    2023  

    Abstract: Objective: Training of surgical procedures on awake patients, such as medialization thyroplasty, poses challenges to educators and trainees. Three-dimensionally (3D)-printed simulators provide opportunity to practice in low-stakes settings. We present ... ...

    Abstract Objective: Training of surgical procedures on awake patients, such as medialization thyroplasty, poses challenges to educators and trainees. Three-dimensionally (3D)-printed simulators provide opportunity to practice in low-stakes settings. We present the first 3D-printed thyroplasty simulator incorporating a cartilaginous framework, endolaryngeal soft tissue housed in a 3D-printed manikin with endoscopic visualization.
    Methods: Male and female laryngeal cartilages and endolarynx molds were 3D printed from an existing open-source design. Cartilage models were made of heat-treated polylactic acid (HTPLA), a material chosen for its thermal stability, allowing drilling. They were combined with molded silicone endolarynges modeling glottic insufficiency. Larynges were set in a 3D-printed head-and-neck manikin with an attached borescope for internal visualization similar to distal chip laryngoscopy. Eight laryngologists evaluated the simulator by drilling a thyroplasty window, inserting an implant for medialization, and rating the model using a modified Michigan Standard Simulation Experience Scale (1 = strongly disagree, 5 = strongly agree).
    Results: The model was well rated in educational value (mean 4.7, standard deviation [SD] 0.3), fidelity (mean 3.8, SD 0.2), and overall value (mean 4.8, SD 0.5). Qualitative assessment concluded the model was anatomically realistic and that HTPLA was a good approximation of the density and texture of thyroid cartilage. The materials for one larynx cost $4.09.
    Conclusion: This high-fidelity 3D-printed simulator demonstrates educational value for thyroplasty training. The low-cost, open-source design has broad implications for universal access to this simulator platform.
    Language English
    Publishing date 2023-11-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 17459-2
    ISSN 1873-4588 ; 1557-8658 ; 0892-1997
    ISSN (online) 1873-4588 ; 1557-8658
    ISSN 0892-1997
    DOI 10.1016/j.jvoice.2023.11.016
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