Voice parameters for difficult mask ventilation evaluation: an observational study

Cao, Shuang; Xia, Ming; Zhou, Runhua; Wang, Jie; Jin, Chen-Yu; Pei, Bei; Zhou, Zhikai; Ye, Qian; Jiang, Hong

doi:10.21037/atm-21-6274

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Research has shown that a difficult airway assessment model constructed by phonetic features (5 vowels and 5 formant frequencies) has value in predicting difficult laryngoscopy and difficult mask ventilation with an AUC of 0.761 and 0.74, respectively and the combination with Modified Mallampati test (MMT) can achieve an even better performance ( 123 , 124 ). In Chinese populations, acoustic features including formant frequencies (f1–f4) and bandwidths (bw1–bw4) were predictive of difficult laryngoscopy and difficult mask ventilation with an AUC of 0.709 and 0.779, respectively ( 125 , 126 ).…”

Section: New Technologies Currently Under Developmentmentioning

confidence: 99%

Expert consensus on difficult airway assessment

Xia¹,

Ma²,

Zuo³

et al. 2023

Hepatobiliary Surg Nutr

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Background Identifying a potentially difficult airway is crucial both in anaesthesia in the operating room (OR) and non-operation room sites. There are no guidelines or expert consensus focused on the assessment of the difficult airway before, so this expert consensus is developed to provide guidance for airway assessment, making this process more standardized and accurate to reduce airway-related complications and improve safety. Methods Seven members from the Airway Management Group of the Chinese Society of Anaesthesiology (CSA) met to discuss the first draft and then this was sent to 15 international experts for review, comment, and approval. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) is used to determine the level of evidence and grade the strength of recommendations. The recommendations were revised through a three-round Delphi survey from experts. Results This expert consensus provides a comprehensive approach to airway assessment based on the medical history, physical examination, comprehensive scores, imaging, and new developments including transnasal endoscopy, virtual laryngoscopy, and 3D printing. In addition, this consensus also reviews some new technologies currently under development such as prediction from facial images and voice information with the aim of proposing new research directions for the assessment of difficult airway. Conclusions This consensus applies to anesthesiologists, critical care, and emergency physicians refining the preoperative airway assessment and preparing an appropriate intubation strategy for patients with a potentially difficult airway.

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Section: New Technologies Currently Under Developmentmentioning

confidence: 99%

Expert consensus on difficult airway assessment

Xia¹,

Ma²,

Zuo³

et al. 2023

Hepatobiliary Surg Nutr

View full text Add to dashboard Cite

show abstract

“…Among the Chinese population, phonetic characteristics incorporating formant frequencies (f1–f4) and bandwidths (bw1–bw4) effectively predicted difficult intubation and mask ventilation with an AUC of 0.709 and 0.779, respectively. [ 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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Voice Analysis as a Method for Preoperatively Predicting a Difficult Airway Based on Machine Learning Algorithms: Original Research Report

Rodiera,

Fortuny,

Valls

et al. 2024

Health Science Reports

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Background and AimsAn unanticipated difficult airway is one of the greatest challenges for anesthesiologists. Proper preoperative airway assessment is crucial to reducing complications. However, current screening tests based on anthropometric features are of uncertain benefit. Therefore, our study explores using voice analysis with machine learning algorithms to predict a difficult airway.MethodsObservational, multicenter study with N = 438 patients initially enrolled at Centro Medico Teknon and Institut Universitari Dexeus (2019–2022) for the research study. After excluding 125 patients, N = 313 were included. Ethics committee approval was obtained. Adults ASA I‐III scheduled for elective procedures under general anesthesia with endotracheal intubation were selected. Patient clinical features and traditional predictive tests were collected. Vowels “A, E, I, O, U” were recorded in normal, flexion, and extension positions. Cormack grade was assessed, and data were analyzed using KNIME, resulting in multiple models based on demographics and voice data. ROC curves and other metrics were evaluated for each model.ResultsAmong multiple models evaluated, two yielded the best performance to predict a difficult airway both exclusively analyzing Cormack I and IV cases which showed the most distinct differences. The variables included in each model were the following: Model 1; included demographic data, vowel “A” in all positions and harmonics of the voice achieving an AUC of 0.91. Model 2; Included demographic data, vowel “O” in normal positions and voice parameters (Shimmer, Jitter, HNR); achieving in an AUC of 0.90. In contrast, models which focused on analyzing all Cormack grades (I, II, III, IV) cases performed less effectively.ConclusionsAcoustic parameters of the voice together with the demographic data of the patients, when introduced into classification algorithms based on machine learning showed promising signs of predicting a difficult airway.

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Voice parameters for difficult mask ventilation evaluation: an observational study

Cited by 3 publications

References 35 publications

Expert consensus on difficult airway assessment

Expert consensus on difficult airway assessment

Scope of artificial intelligence in airway management

Voice Analysis as a Method for Preoperatively Predicting a Difficult Airway Based on Machine Learning Algorithms: Original Research Report

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