Evaluation of anthropometric and ultrasonographic measurements with different machine learning methods in predicting difficult intubation: a prospective observational study.

INTRODUCTION: Difficult intubation is one of the most challenging scenarios to deal with due to increased morbidity and mortality. Machine learning systems can help predict this process in advance. This study aimed to predict whether patients had difficult intubation using machine learning programs for anthropometric and ultrasonographic measurements taken for preoperative airway assessment. MATERIAL: METHOD: Patients over 18 years of age with American Society of Anesthesiologists (ASA) scores I-III who underwent general anesthesia were included. Patients with a history of head/neck surgery, planned thyroidectomy, congenital or acquired airway anomalies morbidly obese patients with BMI > 40 or a known difficult airway were excluded. Preoperative modified mallampati test score and other anthropometric measurements (thyromental distance, neck circumference, mouth opening, sternomental distance) were recorded. Ultrasonographic measurements included the distance from skin to hyoid bone, skin to epiglottis, skin to vocal cords (anterior commissure), skin to trachea, MTT and hyomental distances in neck extension and neutral positions. The dataset was analyzed via eight different machine learning algorithms. RESULTS: We obtained data from 329 patients (62 difficult intubation cases). The Support Vector Machine algorithm achieved the highest performance, with an accuracy of 89.39%, a negative predictive value of 92.7%, and a positive predictive value of 72.7%. Among all evaluated parameters, the modified mallampati score, neck circumference, skin to epiglottic distance and tongue thickness were the strongest predictors of difficult intubation. CONCLUSION: The ability of individual bedside tests, which are commonly used, to predict difficult intubations is limited. Our study demonstrates that incorporating ultrasonographic measurements into a machine learning model, in addition to clinical airway assessments, improves predictive accuracy. Integrating our predictive model into a mobile app could provide a rapid and objective tool for preoperative airway assessment to identify difficult airways and improve patient safety in anesthesia settings. TRIAL REGISTRATION: Prospective Observational.