Application of an artificial intelligence-based airway identification system in tracheal intubation

作者：Huanqing Liu, Xu Yang, Lei Duan, Kaijun Liu, Peng Wu, Xiaona Wang, Hao Tang, Zhen Wang · 发表于：BMC Anesthesiology · 年份：2025 · DOI：10.1186/s12871-025-03504-1 · 被引用次数：2 · 研究领域：Airway Management and Intubation Techniques、Tracheal and airway disorders、Artificial Intelligence in Healthcare and Education

Tracheal intubation is an important component in pre-hospital cardiopulmonary resuscitation and trauma resuscitation, which generally requires higher technical skill and systematic training. Due to the lack of sufficient clinical experience among frontline health care providers and the limited availability of airway management tools, establishing advanced airways promptly remains challenging. To address this challenge, we developed an artificial intelligence model designed for real-time identification of airway structures to assist health care providers in quickly mastering tracheal intubation procedures. A total of 3912 airway‑anatomical images derived from 978 patients at the Daping Hospital of Army Medical University (January 2024 – July 2024) were included. Each patient’s single static image was rotated three times to generate three additional augmented views, resulting a final dataset of 3912 images. In Part 1, five artificial intelligence target detection models were trained with vocal fissures and aryepiglottic fold as identification targets, and model performance was evaluated based on the 784-image test set using precision, recall, F1 value, and mAP. In Part 2, some of the models were deployed on the end-side of a mobile smartphone, incorporating 72 trainee physicians with no intubation experience, grouped under a standardized procedure to complete tracheal intubation on a simulator using video laryngoscopy and mobile tools, comparing the time to glottic exposure and...