Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification

November 17, 2025 updated by: Gizem Demir Şenoğlu, Duzce University
This prospective observational study aims to develop an artificial intelligence model that can automatically determine the Cormack-Lehane classification from video laryngoscopy images in patients undergoing elective surgery. It also aims to predict the risk of difficult intubation based on this classification. The resulting data will evaluate the applicability of AI-supported decision support systems in clinical airway management.

Study Overview

Status

Completed

Conditions

Study Type

Observational

Enrollment (Actual)

132

Contacts and Locations

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

Study Locations

  • Turkey (Türkiye)
    • Merkez
      • Düzce, Merkez, Turkey (Türkiye)
        • Duzce University Faculty of Medicine, Department of Anesthesiology and Reanimation

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

No

Sampling Method

Non-Probability Sample

Study Population

The study population will consist of adult patients undergoing elective surgery under general anesthesia at the operating rooms of Düzce University Medical Faculty Hospital. All patients will have their airways assessed using video laryngoscopy as part of routine anesthesia induction. Only patients without known upper airway pathology will be included.

Patients will be prospectively and consecutively recruited. Video laryngoscopy images will be captured during intubation and used for machine learning analysis. The Cormack-Lehane grade will be independently confirmed by two experienced anesthesiologists. Patients will be classified into normal and difficult intubation groups.

Description

Inclusion Criteria:

  • 18-65 years

Elective surgery

ASA I-II

No upper airway pathology

Exclusion Criteria:

  • Known history of difficult intubation

Morbid obesity (BMI > 40)

Pregnancy

History of upper airway surgery

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
Group 1: Normal Intubation Group
Intubations in patients assessed as Cormack-Lehane (CL) Class 1-2.
Difficult Intubation Group
Intubations in patients evaluated as Cormack-Lehane Class 3-4.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images
Time Frame: Immediately after data collection and model training
The primary outcome is the classification accuracy of the machine learning algorithm in identifying difficult intubation cases (Cormack-Lehane grade 3-4) from video laryngoscopy images, compared with expert anesthesiologists' consensus. Accuracy will be reported as a percentage.
Immediately after data collection and model training

Collaborators and Investigators

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

Sponsor

Duzce University

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 1, 2025

Primary Completion (Actual)

September 30, 2025

Study Completion (Actual)

October 1, 2025

Study Registration Dates

First Submitted

August 21, 2025

First Submitted That Met QC Criteria

August 28, 2025

First Posted (Estimated)

September 3, 2025

Study Record Updates

Last Update Posted (Actual)

November 18, 2025

Last Update Submitted That Met QC Criteria

November 17, 2025

Last Verified

November 1, 2025

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 2025/183

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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