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AI-Based Prediction of Difficult Airway in Bariatric Surgery

NCT07666074 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 340

Last updated 2026-06-24

No results posted yet for this 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.

Conditions

  • Obesity Difficult Airway Airway Management

Interventions

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

  • Elazıg Fethi Sekin Sehir Hastanesi

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-05-21
Primary Completion
2026-09-01
Completion
2026-10-15

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07666074 on ClinicalTrials.gov