Difficult airway is a life-threatening event during anesthesia. Prediction model is helpful to detect high-risk patients and decrease the risk of un-anticipated difficult airway. Present models are usually based on Mallampati grade and the width of mouth open. However, the prediction accuracy is only about 0.7-0.8 in different populations. Present study is designed to investigate if AI-based prediction model using medical imaging parameters (such as CT and MRI) can increase the accuracy of prediction model.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
The accuracy of prediction model based on AI analysis of medical imaging parameters
Timeframe: day 1 (From enrollment to the end of anesthesia induction)