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
This trial does not accept healthy volunteersQualifiers
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 population
Patients with head and neck CT data undergoing surgery under general anesthesia with endotracheal intubation
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
228 Participants
are grouped into 1 trial groupGroup A: Adult patients scheduled for selective surgery
Trial outcomes
Primary outcomes
1
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)Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Mu Dong Liang
Lead sponsor
Peking University First Hospital
Sponsor institution
Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: