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.

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.

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.

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.

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.