[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100598587":3},{"organization":4,"armGroups":7,"interventions":17,"overallOfficials":10,"centralContacts":23,"locations":29,"responsibleParty":43,"collaborators":48,"id":68,"slug":10,"hasResults":69,"nctId":70,"briefTitle":71,"officialTitle":71,"acronym":10,"eligibilityCriteria":72,"healthyVolunteers":69,"sex":73,"minAge":74,"maxAge":10,"enrollmentInfo":75,"targetDuration":10,"studyType":78,"phases":10,"briefSummary":79,"conditions":80,"keywords":10,"overallStatus":31,"whyStopped":10,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":84,"startDateStruct":87,"completionDateStruct":89,"leadSponsor":91,"locationsCount":92},{"fullName":5,"class":6},"Renmin Hospital of Wuhan University","OTHER",[8,12],{"label":9,"type":10,"description":11,"interventionNames":10},"Traditional colonoscopy examination group",null,"the system shows the original colonoscopy video.",{"label":13,"type":10,"description":14,"interventionNames":15},"AI-assisted colonoscopy examination group","The system will present the detected polyp positions as hollow blue and set an alarm box directly on the high-definition monitor to mark whether it is a polyp. Hollow red is used to set an alarm box directly on the high-definition monitor to mark whether it is an adenoma.",[16],"Device: AI models with NBI",[18],{"type":19,"name":20,"description":21,"armGroupLabels":22,"otherNames":10},"DEVICE","AI models with NBI","AI models for detecting intestinal adenoma in magnifying endoscopy with NBI.",[13],[24],{"name":25,"role":26,"phone":27,"phoneExt":10,"email":28},"Mingkai Chen","CONTACT","13720330580","kaimingchen@163.com",[30],{"facility":5,"status":31,"city":32,"state":33,"zip":10,"country":34,"cosmosGeoPoint":35,"geoPoint":40,"contacts":41},"RECRUITING","Wuhan","Hubei","China",{"type":36,"coordinates":37},"Point",[38,39],114.26667,30.58333,{"lat":39,"lon":38},[42],{"name":5,"role":26,"phone":27,"phoneExt":10,"email":28},{"type":44,"investigatorFullName":45,"investigatorTitle":46,"investigatorAffiliation":47,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","ChenMingkai","Professor","Wuhan University",[49,52,54,56,58,60,62,64,66],{"name":50,"class":51},"Beijing Friendship Hospital, Captial Medical University","UNKNOWN",{"name":53,"class":6},"Air Force Military Medical University, China",{"name":55,"class":51},"The Sixth Affiliated Hospital, Sun Yat-sen University",{"name":57,"class":6},"Army Medical University, China",{"name":59,"class":6},"Guizhou Provincial People's Hospital",{"name":61,"class":6},"Shengjing Hospital",{"name":63,"class":51},"The Second Medical Center, Chinese PLA General Hospital",{"name":65,"class":6},"Zhejiang University",{"name":67,"class":6},"Shandong University","100598587",false,"NCT07073430","Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions","Inclusion Criteria:\n\n* Patients aged ≥ 18 years, who need to undergo colonoscopy, regardless of gender.\n* Voluntarily sign the informed consent form\n* Promise to abide by the research procedures and cooperate in the implementation of the entire research process.\n\nExclusion Criteria:\n\n* Patients who has a history of abdominal or pelvic surgery or radiotherapy in the past;\n* Patients who has definite active lower gastrointestinal bleeding.\n* Existing or suspected hereditary colorectal polyposis, inflammatory bowel disease;\n* Uncontrolled hypertension (systolic blood pressure \\> 160 mmHg or diastolic blood pressure \\> 95 mmHg after standardized treatment)\n* There is a history of stroke, coronary artery disease, or vascular disease;\n* Pregnant;\n* Intestinal preparation cannot be carried out.","ALL","18 Years",{"count":76,"type":77},4000,"ESTIMATED","OBSERVATIONAL","This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a \"trinity\" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.",[81,82],"Colorectal Adenoma","Artificial Intelligence (AI)","2026-03-21",{"date":85,"type":86},"2026-03-25","ACTUAL",{"date":88,"type":86},"2023-11-28",{"date":90,"type":77},"2026-10-31",{"name":5,"class":6},1]