[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100626755":3},{"organization":4,"armGroups":7,"interventions":17,"overallOfficials":10,"centralContacts":22,"locations":28,"responsibleParty":43,"collaborators":45,"id":66,"slug":67,"hasResults":68,"nctId":69,"briefTitle":70,"officialTitle":71,"acronym":10,"eligibilityCriteria":72,"healthyVolunteers":68,"sex":73,"minAge":74,"maxAge":75,"enrollmentInfo":76,"targetDuration":79,"studyType":80,"phases":10,"briefSummary":81,"conditions":82,"keywords":90,"overallStatus":30,"whyStopped":10,"lastUpdateSubmitDate":92,"lastUpdatePostDateStruct":93,"startDateStruct":96,"completionDateStruct":98,"leadSponsor":100,"locationsCount":101},{"fullName":5,"class":6},"Changhai Hospital","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"AI group",null,"Diagnosis by Artificial Intelligence model",[13],"Diagnostic Test: Diagnosis by Artificial Intelligence model",{"label":15,"type":10,"description":16,"interventionNames":10},"Clinicians group","Diagnosis by clinicians",[18],{"type":19,"name":11,"description":20,"armGroupLabels":21,"otherNames":10},"DIAGNOSTIC_TEST","To develop an artificial intelligence-based classification management system for pancreatic diseases, achieving automated and precise classification. Contrast-enhanced CT images from all study subjects will be analyzed by the AI system to generate classification results, categorizing patients into three groups: INTERVENTIOM, INTENSIVE SURVEILLANCE or ROUTINE SURVEILLANCE.",[9],[23],{"name":24,"role":25,"phone":26,"phoneExt":10,"email":27},"Beilei Wang, Doctor","CONTACT","+86 13774238083","lilly_wang@126.com",[29],{"facility":5,"status":30,"city":31,"state":10,"zip":32,"country":33,"countryCode":34,"cosmosGeoPoint":35,"geoPoint":40,"contacts":41},"RECRUITING","Shanghai","200433","China","CN",{"type":36,"coordinates":37},"Point",[38,39],121.45806,31.22222,{"lat":39,"lon":38},[42],{"name":24,"role":25,"phone":26,"phoneExt":10,"email":27},{"type":44,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[46,48,51,53,55,57,59,61,64],{"name":47,"class":6},"The First Affiliated Hospital with Nanjing Medical University",{"name":49,"class":50},"The Affiliated People's Hospital of Ningbo University","OTHER_GOV",{"name":52,"class":6},"The Second Affiliated Hospital of Jiaxing University",{"name":54,"class":6},"Shanghai Changzheng Hospital",{"name":56,"class":6},"Xinhua Hospital, Shanghai Jiao Tong University School of Medicine",{"name":58,"class":6},"Shengjing Hospital",{"name":60,"class":6},"Shanghai Fourth People's Hospital Tongji University",{"name":62,"class":63},"The First Affiliated Hospital of Medical School of Zhejiang University","UNKNOWN",{"name":65,"class":63},"Shanghai Fudan University Cancer Center","100626755","ai-powered-precision-decision-making-for-pancreatic-diseases-100626755",false,"NCT07439757","AI-Powered Precision Decision-Making for Pancreatic Diseases","A Multicenter Clinical Study on AI-Powered Precision Decision-Making Management for Pancreatic Diseases Using Contrast-Enhanced CT","Inclusion Criteria:\n\n* Clinically suspected pancreatic disease.\n* Scheduled to undergo contrast-enhanced CT.\n* Signed informed consent form indicating agreement to participate.\n\nExclusion Criteria:\n\n* History of pancreatic surgery.\n* Contraindications to contrast-enhanced CT, including known hypersensitivity to iodinated contrast media or severe renal\u002Fhepatic dysfunction.\n* Suboptimal image quality affecting diagnosis.\n* Concurrent participation in another interventional clinical trial.\n* Unsuitability for participation as determined by the investigator, including but not limited to: pregnancy or lactation, severe psychiatric disorders or cognitive impairment, significant comorbidities that may interfere with study results or patient safety.","ALL","18 Years","80 Years",{"count":77,"type":78},2000,"ESTIMATED","1 Year","OBSERVATIONAL","This multicenter clinical trial evaluates an artificial intelligence (AI) system designed to assist in the diagnosis and management of pancreatic diseases. Using contrast-enhanced CT scans, the study compares the AI's recommendations against the decisions of experienced clinicians to verify the system's accuracy and safety in a real-world setting. Patients are categorized into three management groups: Intervention (surgery\u002Ftreatment), Intensive Surveillance (close monitoring), or Routine Surveillance (standard follow-up). The primary goal is to determine if the AI system can reliably classify patients, reduce the risk of missing malignant lesions, and prevent unnecessary surgeries, thereby improving clinical decision-making for pancreatic conditions.",[83,84,85,86,87,88,89],"Pancreatic Cancer","Diagnose Disease","IPMN, Pancreatic","Pancreatic Cystic Lesions","Chronic Pancreatitis","Pancreatic Neuroendocrine Tumor","Acute Pancreatitis (AP)",[91],"Artificial Intelligence (AI), Deep Learning, Contrast-Enhanced CT, Multicenter Clinical Trial, Real-World Study","2026-02-23",{"date":94,"type":95},"2026-02-27","ACTUAL",{"date":97,"type":78},"2026-03-01",{"date":99,"type":78},"2029-10-31",{"name":5,"class":6},1]