A study on comparing the accuracy of conventional predictor model versus artificial intelligence in predicting difficult intubation in anaesthesia

This study aims to assess the accuracy of conventional predictor model of difficult airway prediction (including parameters like BMI , neck circumference, thyromental distance, interincisor gap, mallampatti classification, age  and head and neck movements) and artificial intelligence model using random forest classifier. Using a Macintosh blade of size 3 or 4 , laryngoscopy will be done and vocal cord is graded according to cormack lehane grading. Grades 1 and 2 are considered as easy and 3 and 4 as difficult airways.