Overview

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.

Eligibility

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.