AI-Based Prediction of Difficult Airway in Bariatric Surgery (AI-Airway)

June 18, 2026 updated by: Muhammed Başpınar, Elazıg Fethi Sekin Sehir Hastanesi

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

340

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Turkey (Türkiye)
    • Elâzığ
      • Elâzığ, Elâzığ, Turkey (Türkiye), 23100
        • Recruiting
        • Fethi Sekin City Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population consists of adult obese patients (BMI ≥ 35 kg/m²) undergoing elective bariatric surgery under general anesthesia in a tertiary academic medical center. This population represents individuals at a higher baseline risk for difficult airway management and intubation.

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Bariatric Surgery Patients
Patients scheduled for elective bariatric surgery under general anesthesia who undergo preoperative airway assessment using clinical and morphometric predictors.
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.
Other Names:
  • Upper Lip Bite Test
  • Modified Mallampati Score
  • Thyromental Distance
  • Sternomental Distance
  • Cormack-Lehane Grading

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Intraoperative (assessed during the primary intubation attempt)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of Intubation Attempts
Time Frame: Intraoperative
Total number of direct laryngoscopy attempts required to achieve successful tracheal intubation.
Intraoperative
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.
Intraoperative

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Elazıg Fethi Sekin Sehir Hastanesi

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 21, 2026

Primary Completion (Estimated)

September 1, 2026

Study Completion (Estimated)

October 15, 2026

Study Registration Dates

First Submitted

June 18, 2026

First Submitted That Met QC Criteria

June 18, 2026

First Posted (Actual)

June 24, 2026

Study Record Updates

Last Update Posted (Actual)

June 24, 2026

Last Update Submitted That Met QC Criteria

June 18, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2026/30-28

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

De-identified individual participant data (including preoperative clinical/morphometric airway measurements and intraoperative Cormack-Lehane grades) underlying the results reported in the final publication will be shared to promote transparency and reproducibility in machine learning models.

IPD Sharing Access Criteria

Data will be shared upon reasonable request with qualified researchers who provide a methodologically sound proposal. Proposals should be directed to the corresponding author's email. To gain access, data requestors will need to sign a data access agreement.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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