Although there is no related research on the evaluation of difficult airways by ultrasound features based on artificial intelligence, the investigators guess that the evaluation of ultrasound features based on artificial intelligence can make further breakthroughs in difficult airway early warning systems. Therefore, this project intends to use AI technology to extract and analyze the ultrasound features of the subjects, evaluate the correlation between the ultrasound features of the subjects and the occurrence of difficult airways, and construct possible diagnostic models to evaluate AI ultrasound feature recognition in the prediction of difficult airways. The effect and application value of this method are expected to be more intelligent and accurate for early warning of difficult airways in clinical anesthesia.
Study Type
OBSERVATIONAL
Enrollment
4,000
Ultrasonic test to the patient's head, neck and jaw area.
Shanghai 9Th Hospital
Shanghai, Shanghai Municipality, China
RECRUITINGDiagnosed as a difficult airway
Cormack-Lehane was used for grading the best glottic view. In grade the entire glottis was visible; in grade 2 a portion of the glottis was visible; in grade 3 only the epiglottis could be seen; and in grade 4 the epiglottis was not visible. A score of 3-4 indicated difficult video laryngoscopy
Time frame: 0-10minutes after intubation
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