AI-based Prediction Model of Difficult Tracheal Intubation Using Medical Image Parameters
ConditionDifficult Airway
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorMu Dong Liang
About this trial
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.
Eligibility criteria
Qualifiers
age ≥18 years old;
surgical patients undergoing general anesthesia with endotracheal intubation;
with head and neck CT examination results
Consent to participate in the study.
Disqualifiers
The presence of laryngeal edema;
The presence of airway stenosis, including internal airway stenosis (such as foreign body or tumor) or stenosis caused by external tracheal mass compression;
tracheo-esophageal fistula;
severe gastroesophageal reflux;
Trial design
Treatments tested in this trial
- Not listed
Trial groups
228 Participants
are grouped into 1 trial groupSponsors and collaborators
Mu Dong Liang
Lead sponsor
Peking University First Hospital
Sponsor institution
Source ClinicalTrials.gov