[{"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:":73},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,37,58],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":4,"eligibilityCriteria":14,"healthyVolunteers":15,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":17,"targetDuration":4,"studyType":20,"phases":4,"briefSummary":21,"conditions":22,"keywords":4,"overallStatus":24,"whyStopped":4,"lastUpdateSubmitDate":25,"lastUpdatePostDateStruct":26,"startDateStruct":29,"completionDateStruct":31,"leadSponsor":33,"locationsCount":36},"100643739","clinical-application-value-of-deep-learning-based-opportunistic-screening-for-malignant-tumors-on-routine-non-contrast-chest-abdomen-pelvis-ct-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":18,"type":19},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.",[23],"Tumor","NOT_YET_RECRUITING","2026-06-05",{"date":27,"type":28},"2026-06-10","ACTUAL",{"date":30,"type":19},"2026-07",{"date":32,"type":19},"2028-07",{"name":34,"class":35},"Lian Yang","OTHER",1,{"id":38,"slug":39,"hasResults":11,"nctId":40,"briefTitle":41,"officialTitle":41,"acronym":4,"eligibilityCriteria":42,"healthyVolunteers":11,"sex":16,"minAge":43,"maxAge":4,"enrollmentInfo":44,"targetDuration":4,"studyType":20,"phases":4,"briefSummary":46,"conditions":47,"keywords":4,"overallStatus":49,"whyStopped":4,"lastUpdateSubmitDate":50,"lastUpdatePostDateStruct":51,"startDateStruct":53,"completionDateStruct":55,"leadSponsor":57,"locationsCount":36},"100614041","the-clinical-value-of-deep-learning-based-reconstruction-techniques-in-cardiac-mri-scanning-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":45,"type":19},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",[48],"Cardiac Disease","RECRUITING","2025-11-27",{"date":52,"type":28},"2025-12-10",{"date":54,"type":28},"2025-08-01",{"date":56,"type":19},"2026-08-01",{"name":34,"class":35},{"id":59,"slug":60,"hasResults":11,"nctId":61,"briefTitle":62,"officialTitle":62,"acronym":4,"eligibilityCriteria":63,"healthyVolunteers":11,"sex":16,"minAge":43,"maxAge":4,"enrollmentInfo":64,"targetDuration":4,"studyType":20,"phases":4,"briefSummary":66,"conditions":67,"keywords":4,"overallStatus":49,"whyStopped":4,"lastUpdateSubmitDate":50,"lastUpdatePostDateStruct":69,"startDateStruct":70,"completionDateStruct":71,"leadSponsor":72,"locationsCount":36},"100614040","clinical-value-of-deep-learning-reconstruction-technology-in-ankle-mri-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":65,"type":19},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.",[68],"Ankle Trauma",{"date":52,"type":28},{"date":54,"type":28},{"date":56,"type":19},{"name":34,"class":35},""]