Artificial Intelligence-Assisted Ultrasound Assessment of Laryngeal Mask Airway Placement

This prospective observational study aims to develop and evaluate artificial intelligence-based models for the assessment of laryngeal mask airway (LMA) placement in adult patients undergoing elective surgery under general anesthesia. Following LMA insertion, standardized airway ultrasound images will be obtained and fiberoptic assessment will be performed as the anatomical reference standard. Fiberoptic findings will be classified as optimal (Brimacombe grades 3-4) or suboptimal (grades 1-2). Clinical and quantitative airway ultrasound variables will also be recorded. The predictive performance of tabular, image-only, and multimodal artificial intelligence models will be evaluated for identifying optimal versus suboptimal LMA placement.