Evaluation of the association between voice formants and difficult facemask ventilation

Carvalho, Clístenes Cristian de; Silva, Danielle Melo da; Carvalho, Antonio Deusany de; Nóbrega, Fernando Jorge Firmino; Orange, Flávia Augusta de

doi:10.1097/eja.0000000000001108

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…Our study differs from the previous work by de Carvalho et al (22). To our knowledge, this study was the first attempt to analyze the association between voice features and DMV in a Chinese population.…”

Section: Discussioncontrasting

confidence: 60%

“…Formant is one of the most important parameters in the acoustic analysis, representing the vocal tract resonance, and relating to the upper airway anatomy (18,19). At present, only studies reported by de Carvalho et al (22,23) have provided some evidence supporting an association between formant frequencies and difficult airway. Considering the influence of different races on pronunciation manner and the incidence of DMV, and the scarcity of literature on the voice features used for MV evaluation, we conducted this study in the Chinese population to confirm the initial findings by de Carvalho et al (22), and to further clarify the potential value of voice analysis of both vowels and consonants to predict DMV.…”

Section: Introductionmentioning

confidence: 72%

“…At present, only studies reported by de Carvalho et al (22,23) have provided some evidence supporting an association between formant frequencies and difficult airway. Considering the influence of different races on pronunciation manner and the incidence of DMV, and the scarcity of literature on the voice features used for MV evaluation, we conducted this study in the Chinese population to confirm the initial findings by de Carvalho et al (22), and to further clarify the potential value of voice analysis of both vowels and consonants to predict DMV. We hypothesized that voice features may be considered an alternative valuable predictor, in addition to already known factors for DMV in patients scheduled for general anesthesia.…”

Section: Introductionmentioning

confidence: 72%

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Voice parameters for difficult mask ventilation evaluation: an observational study

Cao¹,

Xia²,

Zhou³

et al. 2021

Ann Transl Med

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Background: Mask ventilation (MV) is an essential component of airway management. Difficult mask ventilation (DMV) is a major cause for perioperative hypoxic brain injury; however, predicting DMV remains a challenge. This study aimed to determine the potential value of voice parameters as novel predictors of DMV in patients scheduled for general anesthesia. Methods: We included 1,160 adult patients scheduled for elective surgery under general anesthesia. The clinical variables usually reported as predictors of DMV were collected before surgery. Voice sample of phonemes ([a], [o], [e], [i], [u], [ü], [ci], [qi], [chi], [le], [ke], and [en]) were recorded and their formants (f1-f4) and bandwidths (bw1-bw4) were extracted. The definition of DMV was the inability of an unassisted anesthesiologist to ensure adequate ventilation during MV under general anesthesia. Univariate and multivariate logistic regression analyses were used to explore the association between voice parameters and DMV. The predictive value of the voice parameters was evaluated by assessment of area under the curve (AUC) of receiver operating characteristic (ROC) curves of a stepwise forward model. Results:The prevalence of DMV was 218/1,160 (18.8%). The AUC of the stepwise forward model (including o_f4, e_bw2, i_f3, u_pitch, u_f1, u_f4, ü_bw4, ci_f1, qi_f1, qi_f4, qi_bw4, chi_f1, chi_bw2, chi_ bw4, le_pitch, le_bw3, ke_bw2, en_pitch, and en_f2, en_bw4) attained a value of 0.779. The sensitivity and specificity of the model were 75.0% and 71.0%, respectively.Conclusions: Voice parameters may be considered as alternative predictors of DMV, but additional studies are needed to confirm the initial findings.

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Section: Discussioncontrasting

confidence: 60%

Section: Introductionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 72%

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Voice parameters for difficult mask ventilation evaluation: an observational study

Cao¹,

Xia²,

Zhou³

et al. 2021

Ann Transl Med

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“…[ 27 28 ] A step further would be to combine acoustic analysis with multi-lingual machine translation (MMT) techniques, which is thought to improve the model’s accuracy. [ 29 30 ]…”

Section: Ai In Difficult Airway Assessment and Managementmentioning

confidence: 99%

Scope of artificial intelligence in airway management

Naik,

Mathew,

Kundra

2024

Indian Journal of Anaesthesia

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The evolution of artificial intelligence (AI) systems in the field of anaesthesiology owes to notable advancements in data processing, databases, algorithmic programs, and computation power. Over the past decades, its accelerated progression has enhanced safety in anaesthesia by improving the efficiency of equipment, perioperative risk assessments, monitoring, and drug administration systems. AI in the field of anaesthesia aims to improve patient safety, optimise resources, and improve the quality of anaesthesia management in all phases of perioperative care. The use of AI is likely to impact difficult airway management and patient safety considerably. AI has been explored to predict difficult intubation to outperform conventional airway examinations by integrating subjective factors, such as facial appearance, speech features, habitus, and other poorly known features. This narrative review delves into the status of AI in airway management, the most recent developments in this field, and its future clinical applications.

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“…However, there is dearth of studies investigating the potential role of acoustic features in evaluating a difficult airway. de Carvalho et al reported that vowel formants exhibited a significant association with difficult laryngoscopy (14) and difficult facemask ventilation (15). Therefore, formant frequencies and bandwidths during phonation may provide anatomical and functional information about the superior airway and hold a promising role in the prediction of difficult airway.…”

Section: Introductionmentioning

confidence: 99%

Acoustic features as novel predictors of difficult laryngoscopy in orthognathic surgery: an observational study

Xia¹,

Cao²,

Zhou³

et al. 2021

Ann Transl Med

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Background: The evaluation of the difficult intubation is an important process before anaesthesia. The unanticipated difficult intubation is associated with morbidity and mortality. This study aimed to determine whether acoustic features are valuable as an alternative method to predict difficult laryngoscopy (DL) in patients scheduled to undergo orthognathic surgery. Methods: This study included 225 adult patients who were undergoing elective orthognathic surgery under general anaesthesia with tracheal intubation. Preoperatively, clinical airway evaluation was performed, and the acoustic data were collected. Twelve phonemes {[a], [o], [e], [i], [u], [ü], [ci], [qi], [chi], [le], [ke], and [en]} were recorded, and their formants (f1-f4) and bandwidths (bw1-bw4) were extracted. Difficult laryngoscopy was defined as direct laryngoscopy with a Cormack-Lehane grade of 3 or 4. Univariate and multivariate logistic regression analyses were used to examine the associations between acoustic features and DL. Results: Difficult laryngoscopy was reported in 59/225 (26.2%) patients. The area under the curve (AUC)of the backward stepwise model including en_f2 [odds ratio (OR)

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Evaluation of the association between voice formants and difficult facemask ventilation

Cited by 6 publications

References 6 publications

Voice parameters for difficult mask ventilation evaluation: an observational study

Voice parameters for difficult mask ventilation evaluation: an observational study

Scope of artificial intelligence in airway management

Acoustic features as novel predictors of difficult laryngoscopy in orthognathic surgery: an observational study

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