AI-Based Prediction of Difficult Airway in Bariatric Surgery

Condition(s) studied

Investigational drug(s) / intervention(s)

Preoperative Airway Assessment and Direct Laryngoscopy: Measurement of preoperative airway parameters including Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance, and sternomental distance. Intraoperative airway view is graded using the Cormack-Lehane classification during standard direct laryngoscopy.

Study summary

The aim of this prospective study is to evaluate the accuracy of artificial intelligence (AI) and machine learning algorithms in predicting difficult airways in patients undergoing bariatric surgery. Preoperative airway assessments, including the Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance (TMD), and sternomental distance (SMD), will be recorded. The study investigates whether AI models can provide higher sensitivity and specificity in predicting difficult intubation compared to traditional clinical scoring systems in the obese patient population.

Eligibility

Primary outcome measure(s)

• Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation — Intraoperative (assessed during the primary intubation attempt)
  The predictive performance of the AI model will be evaluated by comparing its preoperative difficult airway prediction against the actual intraoperative direct laryngoscopy view. The intraoperative view is graded using the Cormack-Lehane classification system. Grades 3 and 4 are clinically defined as difficult intubation, while Grades 1 and 2 are defined as easy intubation. The primary metric of diagnostic accuracy will be the Area Under the Receiver Operating Characteristic (AUC-ROC) curve.

Trial sites (1)

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