This paper is in the following e-collection/theme issue:

New Methods (901) Machine Learning (1370) Artificial Intelligence (1186) Computer-Aided Diagnosis (94)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25247, first published .
Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study

Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study

Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study

Authors of this article:

Hao-Chun Hu1, 2, 3 Author Orcid Image ;   Shyue-Yih Chang4 Author Orcid Image ;   Chuen-Heng Wang5 Author Orcid Image ;   Kai-Jun Li2 Author Orcid Image ;   Hsiao-Yun Cho2, 6 Author Orcid Image ;   Yi-Ting Chen5 Author Orcid Image ;   Chang-Jung Lu4 Author Orcid Image ;   Tzu-Pei Tsai4 Author Orcid Image ;   Oscar Kuang-Sheng Lee1, 7, 8, 9 Author Orcid Image
Article Authors Cited by (35) Tweetations (8) Metrics

Journals

  1. Chen W, Tsao Y, Lai J, Hung C, Liu Y, Liu C. Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach. Measurement Science Review 2022;22(3):107 View
  2. Uloza V, Maskeliunas R, Pribuisis K, Vaitkus S, Kulikajevas A, Damasevicius R. An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing. Applied Sciences 2022;12(19):9748 View
  3. Compton E, Cruz T, Andreassen M, Beveridge S, Bosch D, Randall D, Livingstone D. Developing an Artificial Intelligence Tool to Predict Vocal Cord Pathology in Primary Care Settings. The Laryngoscope 2023;133(8):1952 View
  4. Yu X, Zhou S, Zou H, Wang Q, Liu C, Zang M, Liu T. Survey of deep learning techniques for disease prediction based on omics data. Human Gene 2023;35:201140 View
  5. Wandell G, Law A, Maxin A, Ha V, Wilson E, Nash M, Merati A, Whipple M, Meyer T. Defining the Performance of Clinician's Ability to Screen for Laryngeal Mass From Voice. Otolaryngology–Head and Neck Surgery 2023;168(6):1371 View
  6. Peterson Q, Fei T, Sy L, Froeschke L, Mendelsohn A, Berke G, Peterson D. Correlating Perceptual Voice Quality in Adductor Spasmodic Dysphonia With Computer Vision Assessment of Glottal Geometry Dynamics. Journal of Speech, Language, and Hearing Research 2022;65(10):3695 View
  7. Seok J, Kwon T. Artificial Intelligence for Clinical Research in Voice Disease. Journal of The Korean Society of Laryngology, Phoniatrics and Logopedics 2022;33(3):142 View
  8. Islam R, Abdel-Raheem E, Tarique M. A Novel Pathological Voice Identification Technique through Simulated Cochlear Implant Processing Systems. Applied Sciences 2022;12(5):2398 View
  9. Wang H, Zhang Z, Zhu Q, Wang X, Dong Z, Men G, Wang J, Lei J, Wang W. Batch skeleton extraction from ESPI fringe patterns using pix2pix conditional generative adversarial network. Optical Review 2022;29(2):97 View
  10. Kang H, Kang J, Lee S, Sim H. Applications and Performances of Artificial Intelligence in Assessment and Diagnosis of Communication Disorders: A Systematic Review of the Literatures. Communication Sciences & Disorders 2022;27(3):703 View
  11. Sakthivel S, Prabhu V, Jhaveri R. Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy. Journal of Healthcare Engineering 2022;2022:1 View
  12. Wang X, Wang T, Ding B. Voice Recognition and Evaluation of Vocal Music Based on Neural Network. Computational Intelligence and Neuroscience 2022;2022:1 View
  13. Calà F, Frassineti L, Manfredi C, Dejonckere P, Messina F, Barbieri S, Pignataro L, Cantarella G. Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters. Bioengineering 2023;10(4):426 View
  14. Suvvari T. The Role of Artificial Intelligence in Diagnosis and Management of Laryngeal Disorders. Ear, Nose & Throat Journal 2023:014556132311750 View
  15. Marchese M, Sensoli F, Campagnini S, Cianchetti M, Nacci A, Ursino F, D’Alatri L, Galli J, Carrozza M, Paludetti G, Mannini A. Artificial intelligence for the recognition of benign lesions of vocal folds from audio recordings. Acta Otorhinolaryngologica Italica 2023;43(5):317 View
  16. Liu G, Hodges J, Yu J, Sung C, Erickson‐DiRenzo E, Doyle P. End‐to‐end deep learning classification of vocal pathology using stacked vowels. Laryngoscope Investigative Otolaryngology 2023;8(5):1312 View
  17. Yu Y, Niu Q, Li X, Xue J, Liu W, Lin D. A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications. Micromachines 2023;14(6):1253 View
  18. Kim H, Park H, Park D, Im S, Lee S. Non-invasive way to diagnose dysphagia by training deep learning model with voice spectrograms. Biomedical Signal Processing and Control 2023;86:105259 View
  19. Watase T, Omiya Y, Tokuno S. Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study. JMIR Biomedical Engineering 2023;8:e50924 View
  20. Tessler I, Primov-Fever A, Soffer S, Anteby R, Gecel N, Livneh N, Alon E, Zimlichman E, Klang E. Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review. European Archives of Oto-Rhino-Laryngology 2024;281(2):863 View
  21. Lee J, Hamill C, Shnayder Y, Buczek E, Kakarala K, Bur A. Exploring the Role of Artificial Intelligence Chatbots in Preoperative Counseling for Head and Neck Cancer Surgery. The Laryngoscope 2024;134(6):2757 View
  22. Barlow J, Sragi Z, Rivera‐Rivera G, Al‐Awady A, Daşdöğen Ü, Courey M, Kirke D. The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review. Otolaryngology–Head and Neck Surgery 2024;170(6):1531 View
  23. Lee J, Seok J, Kim J, Kim H, Kwon T. Evaluating the Diagnostic Potential of Connected Speech for Benign Laryngeal Disease Using Deep Learning Analysis. Journal of Voice 2024 View
  24. Gupta R, Gunjawate D, Nguyen D, Jin C, Madill C. Voice disorder recognition using machine learning: a scoping review protocol. BMJ Open 2024;14(2):e076998 View
  25. Kim H, Song J, Park S, Lee Y. Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis. Scientific Reports 2024;14(1) View
  26. Nwosu O, Naunheim M. Artificial Intelligence in Laryngology, Broncho-Esophagology, and Sleep Surgery. Otolaryngologic Clinics of North America 2024 View
  27. Kim J, Seok J, Lee J, Lee J, Kwon T. Deep-Learning-Based Segmentation of Predefined Chunks in Connected Speech: A Retrospective Analysis. Journal of The Korean Society of Laryngology, Phoniatrics and Logopedics 2024;35(1):15 View
  28. Low D, Rao V, Randolph G, Song P, Ghosh S, Li-Jessen N. Identifying bias in models that detect vocal fold paralysis from audio recordings using explainable machine learning and clinician ratings. PLOS Digital Health 2024;3(5):e0000516 View
  29. Nobel S, Swapno S, Islam M, Safran M, Alfarhood S, Mridha M. A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method. Scientific Reports 2024;14(1) View

Books/Policy Documents

  1. Kim C, Lee S, Lee K. Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. View