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

N/ACompletedNCT07152093

Duzce University132 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

132

Conditions

Difficult Airway Intubation

Eligibility

Sex: ALLMin age: 18 YearsMax age: 65 Years

Medical Language ↔ Plain English

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)

Outcomes

Primary Outcomes

Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images

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.

Time frame: Immediately after data collection and model training

Data from ClinicalTrials.gov

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