[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100626755":3},{"organization":4,"armGroups":7,"interventions":17,"overallOfficials":10,"centralContacts":22,"locations":28,"responsibleParty":42,"collaborators":44,"id":65,"slug":10,"hasResults":66,"nctId":67,"briefTitle":68,"officialTitle":69,"acronym":10,"eligibilityCriteria":70,"healthyVolunteers":66,"sex":71,"minAge":72,"maxAge":73,"enrollmentInfo":74,"targetDuration":77,"studyType":78,"phases":10,"briefSummary":79,"conditions":80,"keywords":88,"overallStatus":30,"whyStopped":10,"lastUpdateSubmitDate":90,"lastUpdatePostDateStruct":91,"startDateStruct":94,"completionDateStruct":96,"leadSponsor":98,"locationsCount":99},{"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,"cosmosGeoPoint":34,"geoPoint":39,"contacts":40},"RECRUITING","Shanghai","200433","China",{"type":35,"coordinates":36},"Point",[37,38],121.45806,31.22222,{"lat":38,"lon":37},[41],{"name":24,"role":25,"phone":26,"phoneExt":10,"email":27},{"type":43,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[45,47,50,52,54,56,58,60,63],{"name":46,"class":6},"The First Affiliated Hospital with Nanjing Medical University",{"name":48,"class":49},"The Affiliated People's Hospital of Ningbo University","OTHER_GOV",{"name":51,"class":6},"The Second Affiliated Hospital of Jiaxing University",{"name":53,"class":6},"Shanghai Changzheng Hospital",{"name":55,"class":6},"Xinhua Hospital, Shanghai Jiao Tong University School of Medicine",{"name":57,"class":6},"Shengjing Hospital",{"name":59,"class":6},"Shanghai Fourth People's Hospital Tongji University",{"name":61,"class":62},"The First Affiliated Hospital of Medical School of Zhejiang University","UNKNOWN",{"name":64,"class":62},"Shanghai Fudan University Cancer Center","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":75,"type":76},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.",[81,82,83,84,85,86,87],"Pancreatic Cancer","Diagnose Disease","IPMN, Pancreatic","Pancreatic Cystic Lesions","Chronic Pancreatitis","Pancreatic Neuroendocrine Tumor","Acute Pancreatitis (AP)",[89],"Artificial Intelligence (AI), Deep Learning, Contrast-Enhanced CT, Multicenter Clinical Trial, Real-World Study","2026-02-23",{"date":92,"type":93},"2026-02-27","ACTUAL",{"date":95,"type":76},"2026-03-01",{"date":97,"type":76},"2029-10-31",{"name":5,"class":6},1]