Artificial Intelligence-Assisted Ultrasound Confirmation of Laryngeal Mask Airway Placement Using Fiberoptic Assessment as the Reference Standard: A Prospective Diagnostic Accuracy Study
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.
Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification
Comparison Between Blind And Ultrasound Guided Laryngeal Mask Airway Insertion
Assessment Of Difficult Airway Predictors: A Prospective Comparison Of Upper Airway Ultrasound And Conventional Anthropometric Measures
Airway Coach Project: Prediction of Videolaryngoscopy Strategy With Clinical and Ultrasound Parameters (Multicentric)