Key result
Imaging and artificial intelligence technologies for difficult airway assessment are superior to traditional methods, though limited by radiation, costs, equipment needs, and immature evidence.
Why the study?
Current difficult airway assessment methods have low accuracy and complex processes, leading to persistent perioperative complications, while imaging and AI methods face clinical limitations and require synthesis.
Does imaging and AI technology improve the accuracy of difficult airway assessment compared to traditional methods in patients undergoing general anesthesia?
Does imaging and AI technology improve the accuracy of difficult airway assessment compared to traditional methods in patients undergoing general anesthesia?
Imaging and AI technologies show promise in improving the accuracy of difficult airway assessment over traditional methods, though practical and technological limitations currently hinder widespread clinical application.
Current difficult airway assessments lack accuracy; leaves open need for validated, simpler predictive tools before practice change.
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.
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Wang et al. (2021) conducted a review in Difficult airway. Imaging and artificial intelligence technology vs. Traditional assessment methods was evaluated. Imaging and artificial intelligence technologies for difficult airway assessment are superior to traditional methods, though limited by radiation, costs, equipment needs, and immature evidence.
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