[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100618033":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":10,"centralContacts":26,"locations":32,"responsibleParty":47,"collaborators":10,"id":49,"slug":50,"hasResults":51,"nctId":52,"briefTitle":53,"officialTitle":54,"acronym":10,"eligibilityCriteria":55,"healthyVolunteers":51,"sex":56,"minAge":57,"maxAge":10,"enrollmentInfo":58,"targetDuration":10,"studyType":61,"phases":10,"briefSummary":62,"conditions":63,"keywords":10,"overallStatus":35,"whyStopped":10,"lastUpdateSubmitDate":66,"lastUpdatePostDateStruct":67,"startDateStruct":70,"completionDateStruct":72,"leadSponsor":74,"locationsCount":75},{"fullName":5,"class":6},"Ruijin Hospital","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Model training and validation cohorts",null,"A deep learning model is trained using the training dataset and validated with the internal validation set.",[13],"Other: Diagnostic",{"label":15,"type":10,"description":16,"interventionNames":17},"Prospective test cohort","Patients are prospectively enrolled, nasopharyngolaryngoscopy examination videos are collected, and the video data are processed to form a prospective test dataset, which is then used for testing.",[13],[19,23],{"type":6,"name":20,"description":21,"armGroupLabels":22,"otherNames":10},"Diagnostic","The deep learning model is trained using the training dataset and tested with the internal validation set.",[9],{"type":6,"name":20,"description":24,"armGroupLabels":25,"otherNames":10},"The prospective dataset is used for the comparative testing of the model and physicians.",[15],[27],{"name":28,"role":29,"phone":30,"phoneExt":10,"email":31},"Bin Ye, MD PhD","CONTACT","+8615216616895","aydyebin@126.com",[33],{"facility":34,"status":35,"city":36,"state":10,"zip":10,"country":37,"countryCode":38,"cosmosGeoPoint":39,"geoPoint":44,"contacts":45},"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","RECRUITING","Shanghai","China","CN",{"type":40,"coordinates":41},"Point",[42,43],121.45806,31.22222,{"lat":43,"lon":42},[46],{"name":28,"role":29,"phone":30,"phoneExt":10,"email":31},{"type":48,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100618033","ai-system-for-anatomic-recognition--lesion-detection-in-nasopharyngolaryngoscopy-a-prospective-study-100618033",false,"NCT07326358","AI System for Anatomic Recognition & Lesion Detection in Nasopharyngolaryngoscopy: A Prospective Study","Development and Validation of an Artificial Intelligence System for Anatomic Site Recognition and Lesion Detection Based on Electronic Nasopharyngolaryngoscopic Images: A Prospective Multicenter Study","Inclusion Criteria:\n\n* Age ≥ 18 years;\n* Underwent standard electronic nasopharyngolaryngoscopy;\n* Patients who underwent biopsy sampling have a clear pathological diagnosis;\n* Signed a written informed consent form.\n\nExclusion Criteria:\n\n* Image quality is substandard with severe motion artifacts;\n* Lesion images are unclear and incomplete.","ALL","18 Years",{"count":59,"type":60},500,"ESTIMATED","OBSERVATIONAL","An artificial intelligence-assisted system is trained and validated by collecting nasopharyngolaryngoscopy images from patients.",[64,65],"Nasopharyngeal Neoplasms","Laryngeal Disease","2025-12-26",{"date":68,"type":69},"2026-01-08","ACTUAL",{"date":71,"type":69},"2025-12-12",{"date":73,"type":60},"2027-03-31",{"name":5,"class":6},1]