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.

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

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.