Literature DB >> 21864051

Artificial neural network analysis to assess hypernasality in patients treated for oral or oropharyngeal cancer.

Marieke de Bruijn1, Louis ten Bosch, Dirk J Kuik, Johannes A Langendijk, C René Leemans, Irma Verdonck-de Leeuw.   

Abstract

OBJECTIVE: Investigation of applicability of neural network feature analysis of nasalance in speech to assess hypernasality in speech of patients treated for oral or oropharyngeal cancer. PATIENTS AND METHODS: Speech recordings of 51 patients and of 18 control speakers were evaluated regarding hypernasality, articulation, intelligibility, and patient-reported speech outcome. Feature analysis of nasalance was performed on /a/, /i/, and /u/ and on the entire stretch of speech.
RESULTS: Nasalance distinguished significantly between patients and controls. Nasalance in /a/ and /i/ predicted best intelligibility, nasalance in /a/ predicted best articulation, and nasalance in /i/ and /u/ predicted best hypernasality.
CONCLUSION: Feature analysis of nasalance in oral or oropharyngeal cancer patients is feasible; prediction of subjective parameters varies between moderate and poor.

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Year:  2011        PMID: 21864051     DOI: 10.3109/14015439.2011.606227

Source DB:  PubMed          Journal:  Logoped Phoniatr Vocol        ISSN: 1401-5439            Impact factor:   1.487


  2 in total

1.  Validity of patient-reported swallowing and speech outcomes in relation to objectively measured oral function among patients treated for oral or oropharyngeal cancer.

Authors:  R N P M Rinkel; I M Verdonck-de Leeuw; R de Bree; N K Aaronson; C R Leemans
Journal:  Dysphagia       Date:  2015-01-28       Impact factor: 3.438

Review 2.  Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls.

Authors:  Shankargouda Patil; Sarah Albogami; Jagadish Hosmani; Sheetal Mujoo; Mona Awad Kamil; Manawar Ahmad Mansour; Hina Naim Abdul; Shilpa Bhandi; Shiek S S J Ahmed
Journal:  Diagnostics (Basel)       Date:  2022-04-19
  2 in total

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