[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100591570":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":11,"locations":17,"responsibleParty":35,"collaborators":10,"id":39,"slug":40,"hasResults":41,"nctId":42,"briefTitle":43,"officialTitle":43,"acronym":10,"eligibilityCriteria":44,"healthyVolunteers":41,"sex":45,"minAge":46,"maxAge":10,"enrollmentInfo":47,"targetDuration":10,"studyType":50,"phases":10,"briefSummary":51,"conditions":52,"keywords":54,"overallStatus":59,"whyStopped":10,"lastUpdateSubmitDate":60,"lastUpdatePostDateStruct":61,"startDateStruct":64,"completionDateStruct":65,"leadSponsor":67,"locationsCount":68},{"fullName":5,"class":6},"Peking University First Hospital","OTHER",[8],{"label":9,"type":10,"description":10,"interventionNames":10},"Adult patients scheduled for selective surgery",null,[12],{"name":13,"role":14,"phone":15,"phoneExt":10,"email":16},"Dongliang Mu Associate professor","CONTACT","+86 13810702725","mudongliang@bjmu.edu.cn",[18],{"facility":5,"status":10,"city":19,"state":20,"zip":21,"country":22,"countryCode":23,"cosmosGeoPoint":24,"geoPoint":29,"contacts":30},"Beijing","Beijing Municipality","100034","China","CN",{"type":25,"coordinates":26},"Point",[27,28],116.39723,39.9075,{"lat":28,"lon":27},[31],{"name":32,"role":14,"phone":33,"phoneExt":10,"email":34},"Dong-Liang Mu","+8601083575138","mudongliang@bjnu.edu.cn",{"type":36,"investigatorFullName":37,"investigatorTitle":38,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"SPONSOR_INVESTIGATOR","Mu Dong Liang","Professor","100591570","ai-based-prediction-model-of-difficult-tracheal-intubation-using-medical-image-parameters-100591570",false,"NCT06982144","AI-based Prediction Model of Difficult Tracheal Intubation Using Medical Image Parameters","Inclusion Criteria:\n\n1. age ≥18 years old;\n2. surgical patients undergoing general anesthesia with endotracheal intubation;\n3. with head and neck CT examination results\n4. Consent to participate in the study.\n\nExclusion Criteria:\n\n1. The presence of laryngeal edema;\n2. The presence of airway stenosis, including internal airway stenosis (such as foreign body or tumor) or stenosis caused by external tracheal mass compression;\n3. tracheo-esophageal fistula;\n4. severe gastroesophageal reflux;\n5. previous upper airway surgery, such as laryngeal cancer radical surgery, snoring surgery, etc.\n\n6）participating in other research projects","ALL","18 Years",{"count":48,"type":49},228,"ESTIMATED","OBSERVATIONAL","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.",[53],"Difficult Airway",[55,56,57,58],"difficult airway","AI-based method","medical imaging","prediction model","NOT_YET_RECRUITING","2025-05-20",{"date":62,"type":63},"2025-05-21","ACTUAL",{"date":60,"type":49},{"date":66,"type":49},"2026-05-30",{"name":37,"class":6},1]