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AI-Based Prediction of Difficult Airway in Bariatric Surgery
Sponsor: Elazıg Fethi Sekin Sehir Hastanesi
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.
Official title: Artificial Intelligence-Based Prediction of Difficult Airway in Bariatric Surgery: A Prospective Evaluation of Preoperative Airway Predictors
Key Details
Gender
All
Age Range
18 Years - 65 Years
Study Type
OBSERVATIONAL
Enrollment
340
Start Date
2026-05-21
Completion Date
2026-10-15
Last Updated
2026-06-24
Healthy Volunteers
Yes
Interventions
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.
Locations (1)
Fethi Sekin City Hospital
Elâzığ, Elâzığ, Turkey (Türkiye)