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.
Study Type
OBSERVATIONAL
Enrollment
228
Peking University First Hospital
Beijing, Beijing Municipality, China
The accuracy of prediction model based on AI analysis of medical imaging parameters
To establish a prediction model for difficult tracheal intubation based on medical imaging parameters (such as CT and MRI) using AI algorithms and verify its predictive accuracy.
Time frame: day 1 (From enrollment to the end of anesthesia induction)
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