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

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

NCT07152093•Duzce University

View on ClinicalTrials.gov

Summary

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.

Eligibility

Age

18 - 65 years

Sex

ALL

Healthy Volunteers

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

Locations (1)

Duzce University Faculty of Medicine, Department of Anesthesiology and Reanimation

Düzce, Merkez, Turkey (Türkiye)

Key Dates

Start Date

May 1, 2025

Primary Completion Date

September 30, 2025

Completion Date

October 1, 2025

Last Updated

November 18, 2025

Enrollment

132

ACTUAL participants

Conditions

Difficult Airway Intubation

Difficult Airway Incidence in Cardiovascular Surgery and a Prediction Model Development

NCT06986187

Difficult Airway IntubationDifficult Airway+3 more

View study

RECRUITINGNA

SEALion: Study on Supplemental Oxygenation Via Nasal Cannula for Young Children During Intubation

NCT06683599

Difficult AirwayDifficult Airway Intubation+1 more

View study

Data Source & Attribution

This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.

ClinicalTrials.gov last update: November 18, 2025Data synced to Clareo: March 29, 2026

Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.

Neither the United States Government nor Clareo Health make any warranties regarding the data. Check ClinicalTrials.gov frequently for updates.

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

Inclusion Criteria

Exclusion Criteria

Difficult Airway Incidence in Cardiovascular Surgery and a Prediction Model Development

SEALion: Study on Supplemental Oxygenation Via Nasal Cannula for Young Children During Intubation

Research on a Machine Learning-Based Predictive Model for Difficult Intubation Using Specific Vocal Characteristics

Thyromental Height Test (TMHT) as a Predictor of Difficult Airway in Obese Patients

Comparison of Ultrasonographic and Anthropometric Measurements in Difficult Airway Evaluation