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Recruiting NCT07666074

AI-Based Prediction of Difficult Airway in Bariatric Surgery

— Observational Obesity Difficult Airway Airway Management

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Preoperative Airway Assessment and Direct Laryngoscopy.
Who it may be relevant to
Registry conditions: Obesity Difficult Airway Airway Management. Basic parameters: 18 years — 65 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Turkey (Türkiye)
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

Artificial Intelligence-Based Prediction of Difficult Airway in Bariatric Surgery: A Prospective Evaluation of Preoperative Airway Predictors

Overview

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.

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.

Primary outcome measures

  • Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation [Time frame: Intraoperative (assessed during the primary intubation attempt)]
Secondary outcome measures (2)
  • Number of Intubation Attempts [Time frame: Intraoperative]
  • Need for Alternative Airway Management Techniques [Time frame: Intraoperative]

Eligibility criteria

Inclusion criteria

  • Adult patients aged 18 to 65 years.
  • Scheduled for elective bariatric surgery under general anesthesia.
  • Body Mass Index (BMI) ≥ 35 kg/m².
  • Consenting to participate in the study.

Exclusion criteria

  • Patients with known upper airway anatomical deformities, head and neck tumors, or a history of head/neck radiotherapy.
  • History of maxillofacial, airway, or cervical spine surgery.
  • Emergency surgeries.
  • Patients requiring planned awake fiberoptic intubation based on obvious preoperative clinical indicators.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model
Cohort

Study locations

Turkey (Türkiye) · 1 center
  • Fethi Sekin City Hospital — Elâzığ

Identifiers

NCT: NCT07666074 · 2026/30-28

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗