[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Lian Yang\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":70},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,36,56],{"id":9,"slug":4,"hasResults":10,"nctId":11,"briefTitle":12,"officialTitle":12,"acronym":4,"eligibilityCriteria":13,"healthyVolunteers":14,"sex":15,"minAge":4,"maxAge":4,"enrollmentInfo":16,"targetDuration":4,"studyType":19,"phases":4,"briefSummary":20,"conditions":21,"keywords":4,"overallStatus":23,"whyStopped":4,"lastUpdateSubmitDate":24,"lastUpdatePostDateStruct":25,"startDateStruct":28,"completionDateStruct":30,"leadSponsor":32,"locationsCount":35},"100643739",false,"NCT07639567","Clinical Application Value of Deep Learning-Based \"Opportunistic Screening\" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT","Inclusion Criteria:\n\n1. Patients with a confirmed diagnosis of the target malignancy who received treatment at our institution;\n2. Diagnostic-quality CT images without substantial metal or motion artifacts and with complete anatomical coverage of the target organ (breast, liver, kidney, or bladder);\n3. Availability of complete pre-treatment non-contrast CT imaging data.\n\nExclusion Criteria:\n\n1. Non-diagnostic image quality;\n2. Absence of a definitive reference-standard diagnosis;\n3. Incomplete clinical or imaging data.",true,"ALL",{"count":17,"type":18},100000,"ESTIMATED","OBSERVATIONAL","This study aims to develop and validate a deep learning-based opportunistic multi-cancer screening system using routine non-contrast chest-abdomen-pelvis CT examinations, including CHANCE-Breast, CHANCE-Liver, CHANCE-Kidney, and CHANCE-Bladder, for the early detection of breast, liver, kidney, and bladder cancers. In addition, the study will assess a human-AI collaborative framework to determine its potential for improving cancer detection and reducing missed diagnoses in clinical practice.",[22],"Tumor","NOT_YET_RECRUITING","2026-06-05",{"date":26,"type":27},"2026-06-10","ACTUAL",{"date":29,"type":18},"2026-07",{"date":31,"type":18},"2028-07",{"name":33,"class":34},"Lian Yang","OTHER",1,{"id":37,"slug":4,"hasResults":10,"nctId":38,"briefTitle":39,"officialTitle":39,"acronym":4,"eligibilityCriteria":40,"healthyVolunteers":10,"sex":15,"minAge":41,"maxAge":4,"enrollmentInfo":42,"targetDuration":4,"studyType":19,"phases":4,"briefSummary":44,"conditions":45,"keywords":4,"overallStatus":47,"whyStopped":4,"lastUpdateSubmitDate":48,"lastUpdatePostDateStruct":49,"startDateStruct":51,"completionDateStruct":53,"leadSponsor":55,"locationsCount":35},"100614041","NCT07274436","The Clinical Value of Deep Learning-Based Reconstruction Techniques in Cardiac MRI Scanning","Inclusion Criteria:\n\n1. Patients requiring cardiac MRI in clinical practice;\n2. Patient age ≥ 18 years;\n3. The patient has signed an informed consent form.\n\nExclusion Criteria:\n\n1. Patients with contraindications to magnetic resonance imaging;\n2. Patients who failed to complete the MRI examination or whose image quality was inadequate for diagnostic requirements;\n3. Other circumstances deemed by clinical trial personnel as unsuitable for participation in this trial;\n4. Subjects or their legal guardians voluntarily requesting to withdraw.","18 Years",{"count":43,"type":18},50,"By enrolling patients who underwent cardiac MR(Magnetic Resonance) examinations at our center and using randomized allocation, the patients were divided into a study group and a control group. The study group underwent scanning using AI-based(Artificial Intelligence-based) cardiac MRI(Magnetic Resonance Imaging) sequences, while the control group was scanned using non-AI cardiac MRI sequences",[46],"Cardiac Disease","RECRUITING","2025-11-27",{"date":50,"type":27},"2025-12-10",{"date":52,"type":27},"2025-08-01",{"date":54,"type":18},"2026-08-01",{"name":33,"class":34},{"id":57,"slug":4,"hasResults":10,"nctId":58,"briefTitle":59,"officialTitle":59,"acronym":4,"eligibilityCriteria":60,"healthyVolunteers":10,"sex":15,"minAge":41,"maxAge":4,"enrollmentInfo":61,"targetDuration":4,"studyType":19,"phases":4,"briefSummary":63,"conditions":64,"keywords":4,"overallStatus":47,"whyStopped":4,"lastUpdateSubmitDate":48,"lastUpdatePostDateStruct":66,"startDateStruct":67,"completionDateStruct":68,"leadSponsor":69,"locationsCount":35},"100614040","NCT07274423","Clinical Value of Deep Learning Reconstruction Technology in Ankle MRI","Inclusion Criteria:\n\n1. Patients undergoing ankle MRI at Wuhan Union Hospital from August 2025 to August 2026;\n2. Aged \\> 18 years old;\n3. Patients who agree to participate in the study and provide signed informed consent;\n4. Presence of a clear history of ankle trauma and related symptoms requiring MRI for diagnosis or evaluation.\n\nExclusion Criteria:\n\n1 .Patients with MR examination contraindications (e.g., implanted metal devices or severe claustrophobia); 2. Patients with prior ankle surgery; 3. Patients who failed to complete MR examination or whose image quality was insufficient for diagnostic purposes.",{"count":62,"type":18},120,"By enrolling patients who underwent ankle MR(Magnetic Resonance) examinations at our center and using randomized allocation, the patients were divided into a study group and a control group. The study group underwent scanning using AI-based(Artificial Intelligence-based) ankle MRI sequences, while the control group was scanned using non-AI ankle MRI sequences.",[65],"Ankle Trauma",{"date":50,"type":27},{"date":52,"type":27},{"date":54,"type":18},{"name":33,"class":34},""]