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NCT Number: NCT07666074

AI-Based Prediction of Difficult Airway in Bariatric Surgery

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.

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Key information

Conditions

Obesity Difficult Airway Airway Management Body Weight Nutrition Disorders Nutritional and Metabolic Diseases Obesity Overnutrition Overweight Pathological Conditions, Signs and Symptoms Signs and Symptoms

Age range

18 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Fethi Sekin City Hospital

Elâzığ, 23100, Turkey (Türkiye)

Location status: Recruiting

Location contact

Muhammed Başpınar, M.D.

CONTACT

[email protected]

+905395831141

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Preoperative Airway Assessment and Direct Laryngoscopy

Diagnostic Test

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.

Other names: Upper Lip Bite Test, Modified Mallampati Score, Thyromental Distance, Sternomental Distance, Cormack-Lehane Grading

Primary outcomes

  1. Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation

    Time frame: 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.

Secondary outcomes

  1. Number of Intubation Attempts

    Time frame: Intraoperative

    Total number of direct laryngoscopy attempts required to achieve successful tracheal intubation.

  2. Need for Alternative Airway Management Techniques

    Time frame: Intraoperative

    The frequency of requiring alternative airway devices or strategies (e.g., video laryngoscope, bougie, or fiberoptic bronchoscope) to secure the airway after a primary direct laryngoscopy.

Study contacts

Contact information is provided by the study sponsor or research team.

Muhammed Başpınar, M.D.

CONTACT

[email protected]

+905395831141

Sponsors and collaborators

Lead sponsor

Elazıg Fethi Sekin Sehir Hastanesi

Other

Registry information

Official study title

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

Acronym: AI-Airway

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jun 24, 2026
Registry last updated
Jun 24, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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