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Background <p>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.</p> Methods <p>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 the first-attempt success rate.</p> Results <p>Among the five deep learning models developed, YOLO was the best-performing model, achieving a precision of 94.3% and 62.4% for identifying glottis fissure and aryepiglottic fold structures, respectively, as well as a recall rate of 87.0% and 55.7%, respectively. Map50 was 0.924. Outcomes of Part 2 showed that trainee physicians using the AI‑assisted intubation system had their average intubation time reduced by 11.1&#xa0;s, and the first‑attempt success rate increased by 19.4%.</p> Conclusions <p>The YOLO model can effectively recognize and label airway anatomical structures, serving as a reliable tool to guide operators in real time to complete tracheal intubation. Its deployment on mobile devices emphasizes its clinical practicality and pre-hospital accessibility.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月11日

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  • Huanqing Liu,
  • Yizhen Xu,
  • Lei Duan,
  • Kaijun Liu,
  • Peng Wu,
  • Xiaona Wang,
  • Hao Tang,
  • Zhen Wang

摘要

Background

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.

Methods

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 the first-attempt success rate.

Results

Among the five deep learning models developed, YOLO was the best-performing model, achieving a precision of 94.3% and 62.4% for identifying glottis fissure and aryepiglottic fold structures, respectively, as well as a recall rate of 87.0% and 55.7%, respectively. Map50 was 0.924. Outcomes of Part 2 showed that trainee physicians using the AI‑assisted intubation system had their average intubation time reduced by 11.1 s, and the first‑attempt success rate increased by 19.4%.

Conclusions

The YOLO model can effectively recognize and label airway anatomical structures, serving as a reliable tool to guide operators in real time to complete tracheal intubation. Its deployment on mobile devices emphasizes its clinical practicality and pre-hospital accessibility.