[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100604485":3},{"organization":4,"armGroups":7,"interventions":26,"overallOfficials":30,"centralContacts":35,"locations":44,"responsibleParty":57,"collaborators":10,"id":61,"slug":62,"hasResults":63,"nctId":64,"briefTitle":65,"officialTitle":65,"acronym":10,"eligibilityCriteria":66,"healthyVolunteers":63,"sex":67,"minAge":10,"maxAge":10,"enrollmentInfo":68,"targetDuration":10,"studyType":71,"phases":10,"briefSummary":72,"conditions":73,"keywords":75,"overallStatus":79,"whyStopped":10,"lastUpdateSubmitDate":80,"lastUpdatePostDateStruct":81,"startDateStruct":84,"completionDateStruct":86,"leadSponsor":88,"locationsCount":89},{"fullName":5,"class":6},"Fudan University","OTHER",[8,14,18,22],{"label":9,"type":10,"description":11,"interventionNames":12},"Cohort for Module 1",null,"Development and Validation of Module for Exclusion of Unqualified Frames in Colonoscopy Videos",[13],"Other: No Intervention: Observational Cohort",{"label":15,"type":10,"description":16,"interventionNames":17},"Cohort for Module 2","Development and Validation of Module for BBPS 0-3 Scoring for Qualified Colonoscopy Images",[13],{"label":19,"type":10,"description":20,"interventionNames":21},"Cohort for Module 3","Development and Validation of Module for Prediction of Hepatic and Splenic Flexure Locations",[13],{"label":23,"type":10,"description":24,"interventionNames":25},"Cohort for Module 4","Development and Validation of Module for Real-Time Prediction of Withdrawal Distance",[13],[27],{"type":6,"name":28,"description":28,"armGroupLabels":29,"otherNames":10},"No Intervention: Observational Cohort",[9,15,19,23],[31],{"name":32,"affiliation":33,"role":34},"Danian Ji, M.D.","Huadong Hospital","STUDY_DIRECTOR",[36,40],{"name":32,"role":37,"phone":38,"phoneExt":10,"email":39},"CONTACT","+86-18019094606","arctg4@163.com",{"name":41,"role":37,"phone":42,"phoneExt":10,"email":43},"Zhiyu Dong, M.D.","+86-18817870866","18817870866@163.com",[45],{"facility":46,"status":10,"city":47,"state":10,"zip":48,"country":49,"countryCode":50,"cosmosGeoPoint":51,"geoPoint":56,"contacts":10},"Huadong hospital, Fudan university","Shanghai","200040","China","CN",{"type":52,"coordinates":53},"Point",[54,55],121.45806,31.22222,{"lat":55,"lon":54},{"type":58,"investigatorFullName":59,"investigatorTitle":60,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Zhijun Bao","Director","100604485","development-and-validation-of-an-artificial-intelligence-assisted-system-for-bowel-cleanliness-assessment-based-on-withdrawal-distance-weighting-100604485",false,"NCT07150130","Development and Validation of an Artificial Intelligence-assisted System for Bowel Cleanliness Assessment Based on Withdrawal Distance Weighting","Inclusion Criteria:\n\n* Clear colonoscopy images suitable for BBPS scoring\n* Complete and clear colonoscopy videos suitable for BBPS scoring\n* Clear colonoscopy videos with a stable UPD-3 positioning system, without signal drift, disappearance, or other disruptions\n\nExclusion Criteria:\n\n* Blurred colonoscopy images\n* Colonoscopy images taken from the small intestine or outside the patient's body\n* Colonoscopy images captured during irrigation or instrument manipulation\n* Colonoscopy images obtained during chromoendoscopy\n* Colonoscopy videos that do not contain the complete withdrawal process\n* Videos in which the UPD-3 colonoscopic positioning system exhibited signal drift, disappearance, or other instability","ALL",{"count":69,"type":70},700,"ESTIMATED","OBSERVATIONAL","To address the limitations of current AI-based systems that rely on the assumption of a \"constant withdrawal speed,\" this study proposes the integration of the UPD-3 endoscopic positioning system. By using colonoscope withdrawal videos in combination with UPD-3 imaging data as training samples, we aim to develop an AI-powered bowel cleanliness assessment system that incorporates \"withdrawal distance\" as a weighting factor. This approach is expected to yield a more reliable, objective, and clinically applicable intelligent assessment system that better aligns with real-world clinical practice and endoscopists' operational habits.",[74],"Colorectal Adenoma",[76,77,78],"Adenoma detection rate","Bowel preparation assessment","Artificial intelligence","NOT_YET_RECRUITING","2025-08-25",{"date":82,"type":83},"2025-09-02","ACTUAL",{"date":85,"type":70},"2025-10-01",{"date":87,"type":70},"2028-10-01",{"name":5,"class":6},1]