Advances in studies on imaging and artificial intelligence technology-assisted difficult airway assessment

Abstract

Endotracheal intubation is an indispensable part to ensure oxygen supply for patients who undergo general anesthesia in surgeries. However, despite great progress and improvement in intubation technology and equipment, the incidence of perioperative complications and disabilities caused by difficult airway has not been well improved, especially for unpredictable difficult airway. At present, the accuracy of the difficult airway assessment methods are not high and the process is complex, so it is our focus to optimize the difficult airway prediction process and improve the prediction accuracy. At present, imaging and artificial intelligence (AI) face recognition technology as an aid to the assessment of difficult airways is significantly better than the traditional assessment methods. However, imaging techniques such as X-ray, CT, MRI, and ultrasound have their own clinical application value, while at the same time have many limitations, such as radiation, medical costs, equipment requirements, and medical staff burden, which prevent them from being widely used in clinical practice. On the other hand, AI technology in difficult airway identification is still in the sprouting stage, with the technology being immature and lacks sufficient research evidence to support it. For this reason, we have reviewed the literature on imaging technology and AI technology-assisted assessment of difficult airway in recent years, and we hope to provide new ideas for further research and to shed light on the emergence of a convenient and accurate method for difficult airway early warning, thus optimizing the clinical workflow, reducing the risk of misclassification, and ensuring the perioperative life safety of patients.