Machine learning models based on ultrasound and physical examination for airway assessment
Revista Española de Anestesiología y Reanimación (English Edition)(2024)
Abstract
Purpose
To demonstrate the utility of machine learning models for predicting difficult airways using clinical and ultrasound parameters.
Methods
This is a prospective non-consecutive cohort of patients undergoing elective surgery. We collected as predictor variables age, sex, BMI, OSA, Mallampatti, thyromental distance, bite test, cervical circumference, cervical ultrasound measurements, and Cormack-Lehanne class after laryngoscopy. We univariate analyzed the relationship of the predictor variables with the Cormack-Lehanne class to design machine learning models by applying the random forest technique with each predictor variable separately and in combination. We found each design's AUC-ROC, sensitivity, specificity, and positive and negative predictive values.
Results
We recruited 400 patients. Cormack-Lehanne patients ≥ III had higher age, BMI, cervical circumference, Mallampati class membership ≥ III, and bite test ≥ II and their ultrasound measurements were significantly higher. Machine learning models based on physical examination obtained better AUC-ROC values than ultrasound measurements but without reaching statistical significance. The combination of physical variables that we call the "Classic Model" achieved the highest AUC-ROC value among all the models [0.75 (0.67-0.83)], this difference being statistically significant compared to the rest of the ultrasound models.
Conclusions
The use of machine learning models for diagnosing VAD is a real possibility, although it is still in a very preliminary stage of development.
Clinical registry
ClinicalTrials.gov: NCT04816435.
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Key words
Ultrasound,Machine learning,Difficult airway,Ecografía,Aprendizaje Automático,Vía aérea difícil
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