PHONOSURGERY AND OTHER VOICE IMPROVEMENT TECHNIQUES.
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ABSTRACT:
SUBMITTER: Kacker SK
PROVIDER: S-EPMC5531033 | biostudies-other | 1997 Apr
REPOSITORIES: biostudies-other
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Project description:BackgroundNormal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems.ObjectiveThis study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest.MethodsThis systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group.ResultsIn the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models.ConclusionsThis SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.
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Project description:Given the transformations in teaching modalities as a result of the COVID-19 pandemic, this study aimed to evaluate the factors associated with vocal self-perception during the pandemic and to investigate the ability of selected variables to predict changes in vocal self-perception in teachers with professional vocal demands during the investigated period. Cross-sectional survey. A total of 1,126 teachers of both genders (ages 19-78 years, average 43.23 years) who were participating in remote teaching activities using their voice answered the online questionnaire. To characterize vocal changes (improvement, worsening or no change), the teachers' self-assessment of their voice quality was considered; the association between the variables reported in the questionnaire and vocal changes was analyzed, as was the capacity of these variables for predicting voice changes. Most of the teachers worked at a public school, reported having no previous voice changes or difficulties with their voice during online classes and indicated that they began to use their voice less often and with reduced intensity during the pandemic. Vocal demands were divided between teaching synchronous online classes, recording video lessons and participating in online meetings. Several variables were associated with voice improvement and worsening, and some had the capacity to predict voice changes: improvement in voice quality was associated with working in a public school or as an autonomous teacher, reduced voice use and, to a lesser extent, participating in online meetings, certain vocal habits and a reduction in vocal symptoms; worsening voice quality was associated with working in a private school, increased voice use, using the voice at a higher intensity, difficulty with the voice in online classes, vocal habits and vocal symptoms. The context of remote education has changed the way teachers communicate during classes, and monitoring is necessary to ensure that necessary adaptations are made to preserve the voice and ensure effective communication.
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Project description:We evaluated the ability of simple and complex surrogate-indices to identify individuals from an overweight/obese cohort with hepatic insulin-resistance (HEP-IR). Five indices, one previously defined and four newly generated through step-wise linear regression, were created against a single-cohort sample of 77 extensively characterised participants with the metabolic syndrome (age 55.6 ± 1.0 years, BMI 31.5 ± 0.4 kg/m(2); 30 males). HEP-IR was defined by measuring endogenous-glucose-production (EGP) with [6-6(2)H(2)] glucose during fasting and euglycemic-hyperinsulinemic clamps and expressed as EGP*fasting plasma insulin. Complex measures were incorporated into the model, including various non-standard biomarkers and the measurement of body-fat distribution and liver-fat, to further improve the predictive capability of the index. Validation was performed against a data set of the same subjects after an isoenergetic dietary intervention (4 arms, diets varying in protein and fiber content versus control). All five indices produced comparable prediction of HEP-IR, explaining 39-56% of the variance, depending on regression variable combination. The validation of the regression equations showed little variation between the different proposed indices (r(2) = 27-32%) on a matched dataset. New complex indices encompassing advanced measurement techniques offered an improved correlation (r = 0.75, P<0.001). However, when validated against the alternative dataset all indices performed comparably with the standard homeostasis model assessment for insulin resistance (HOMA-IR) (r = 0.54, P<0.001). Thus, simple estimates of HEP-IR performed comparable to more complex indices and could be an efficient and cost effective approach in large epidemiological investigations.
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Project description:Auditory verbal hallucinations (AVH) are a cardinal symptom of psychosis but also occur in 6-13% of the general population. Voice perception is thought to engage an internal forward model that generates predictions, preparing the auditory cortex for upcoming sensory feedback. Impaired processing of sensory feedback in vocalization seems to underlie the experience of AVH in psychosis, but whether this is the case in nonclinical voice hearers remains unclear. The current study used electroencephalography (EEG) to investigate whether and how hallucination predisposition (HP) modulates the internal forward model in response to self-initiated tones and self-voices. Participants varying in HP (based on the Launay-Slade Hallucination Scale) listened to self-generated and externally generated tones or self-voices. HP did not affect responses to self vs. externally generated tones. However, HP altered the processing of the self-generated voice: increased HP was associated with increased pre-stimulus alpha power and increased N1 response to the self-generated voice. HP did not affect the P2 response to voices. These findings confirm that both prediction and comparison of predicted and perceived feedback to a self-generated voice are altered in individuals with AVH predisposition. Specific alterations in the processing of self-generated vocalizations may establish a core feature of the psychosis continuum.
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Project description:Study Design Retrospective review. Objective The purpose of this study was to determine the radiographic impact of a transforaminal lumbar interbody fusion (TLIF) versus a cantilever TLIF technique on segmental lordosis, segmental coronal alignment, and disk height. Methods A retrospective review was done of all patients undergoing TLIF procedures from 2006 to 2011 by three spine surgeons. Traditional TLIF versus cantilever TLIF results were compared, and radiographic outcomes were assessed. Results One hundred one patients were included in the study. Patients undergoing the cantilever TLIF procedure had a significantly greater change in segmental lordosis and disk height compared with those who underwent the traditional procedure (p > 0.0001). Conclusions The cantilever TLIF technique can lead to greater change in segmental lordosis based upon radiographic outcomes.
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