Machine learning models based on ultrasound and physical examination for airway assessment

To demonstrate the utility of machine learning models for predicting difficult airways using clinical and ultrasound parameters.

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.

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.

The use of machine learning models for diagnosing VAD is a real possibility, although it is still in a very preliminary stage of development.

ClinicalTrials.gov: NCT04816435.