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

Digital Health Reviews (1289) Machine Learning (1465) Artificial Intelligence (1371) Mobile Health (mhealth) (2941) Telehealth and Telemonitoring (1671)

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 (11) 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 2024:1 View
  9. Cai J, Song Y, Wu J, Chen X. Voice Disorder Classification Using Wav2vec 2.0 Feature Extraction. Journal of Voice 2024 View

Books/Policy Documents

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