Modelos de aprendizaje automático basados en ecografía y exploración física para la evaluación de la vía aérea

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 difficult airways is a real possibility, although it is still in a very preliminary stage of development.

Clinical Registry

ClinicalTrials.gov: NCT04816435.