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

Digital Health Reviews (1724) Machine Learning (2076) Artificial Intelligence (2129) Mobile Health (mhealth) (3464) Telehealth and Telemonitoring (2088)

Published on in Vol 24, No 10 (2022): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38472, first published .
The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis

The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis

The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis

Authors of this article:

Ghada Al-Hussain1 Author Orcid Image ;   Farag Shuweihdi2 Author Orcid Image ;   Haitham Alali3 Author Orcid Image ;   Mowafa Househ4 Author Orcid Image ;   Alaa Abd-alrazaq5 Author Orcid Image
Article Authors Cited by (22) Tweetations (4) Metrics

Journals

  1. Contreras R, Viana M, Fonseca E, dos Santos F, Zanin R, Guido R. An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection. Sensors 2023;23(11):5196 View
  2. 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
  3. Ur Rehman M, Shafique A, Azhar Q, Jamal S, Gheraibia Y, Usman A. Voice disorder detection using machine learning algorithms: An application in speech and language pathology. Engineering Applications of Artificial Intelligence 2024;133:108047 View
  4. 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
  5. Shen H, Cao J, Zhang L, Li J, Liu J, Chu Z, Wang S, Qiao Y. Classification research of TCM pulse conditions based on multi-label voice analysis. Journal of Traditional Chinese Medical Sciences 2024;11(2):172 View
  6. Liu G, Jovanovic N, Sung C, Doyle P. A Scoping Review of Artificial Intelligence Detection of Voice Pathology: Challenges and Opportunities. Otolaryngology–Head and Neck Surgery 2024;171(3):658 View
  7. Wang J, Ouyang H, Jiao R, Cheng S, Zhang H, Shang Z, Jia Y, Yan W, Wu L, Liu W. The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis. npj Digital Medicine 2024;7(1) View
  8. Cordella C, Marte M, Liu H, Kiran S. An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications. Perspectives of the ASHA Special Interest Groups 2025;10(2):432 View
  9. Cai J, Song Y, Wu J, Chen X. Voice Disorder Classification Using Wav2vec 2.0 Feature Extraction. Journal of Voice 2024 View
  10. Alqurashi M, Alshagrawi S. Assessing the Impact of Artificial Intelligence Applications on Diagnostic Accuracy in Saudi Arabian Healthcare: A Systematic Review. The Open Public Health Journal 2025;18(1) View
  11. Kodali M, Kadiri S, Narayanan S, Alku P. The machine learning-based prediction of the sound pressure level from pathological and healthy speech signals. The Journal of the Acoustical Society of America 2025;157(3):1726 View
  12. Brockmann‐Bauser M. How Well Will AI Help Recognize Voice Disorders? A State‐of‐the‐art Review of Current Acoustic Assessment Strategies and Future Applications. World Journal of Otorhinolaryngology - Head and Neck Surgery 2025 View
  13. Timerbulatov I, Savelieva E, Pestova R, Zagidullina I, Timerbulatov R. Assessment of the possibility of acoustic voice analysis. Meditsinskiy sovet = Medical Council 2025;(7):185 View
  14. Balo E, Ökte B, Selvi Balo S. Artificial intelligence in assessment and intervention of speech and language disorders: A literature review. The European Research Journal 2025;11(6):1235 View
  15. Yousef A, Cantor-Cutiva L, Hunter E. Mapping 74 years in acoustic analysis of voice disorders: A bibliometric review and future research directions. Journal of Communication Disorders 2025;117:106555 View
  16. Vanhove A, Graham B, Titareva T, Udomvisawakul A. Classification Performance of Supervised Machine Learning to Predict Human Resource Management Outcomes: A Meta‐Analysis Using Cross‐Classified Multilevel Modeling. Human Resource Management 2025;64(6):1767 View
  17. Kaur P, Chand T, Rani S. Integration of Artificial Intelligence in Laryngeal Cancer Diagnosis and Prognosis: A Comparative Analysis Bridging Traditional Medical Practices with Modern Computational Techniques. Archives of Computational Methods in Engineering 2025 View
  18. Vizza P, Di Ponio A, Timpano G, Bossio R, Tradigo G, Pozzi G, Guzzi P, Veltri P. Through the Speech and Vocal Signals Hidden Secrets: An Explainable Methodology for Neurological Diseases Early Detection. Journal of Healthcare Informatics Research 2025;9(4):533 View
  19. Yousef A, Castillo-Allendes A, Berardi M, Codino J, Rubin A, Hunter E. Screening Voice Disorders: Acoustic Voice Quality Index, Cepstral Peak Prominence, and Machine Learning. Folia Phoniatrica et Logopaedica 2025;77(5):480 View

Books/Policy Documents

  1. Contreras R, Heck G, Viana M, dos Santos Bongarti M, Zamani H, Guido R. Advances in Swarm Intelligence. View

Conference Proceedings

  1. Liu Y, Ji W, Zhou L, Zheng H, Li Y. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Trusted Detection for Parkinson's Disease Based on Multi-Type Speech Fusion View
  2. Yousef A, Hunter E. The 1st International Online Conference on Bioengineering. Machine Learning Classifiers for Voice Health Assessment Under Simulated Room Acoustics View