Ignite Creation Date:
2025-12-25 @ 5:01 AM
Ignite Modification Date:
2025-12-26 @ 4:02 AM
Study NCT ID:
NCT07087418
Status:
RECRUITING
Last Update Posted:
2025-07-28
First Post:
2025-07-20
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AI-Driven Multimodal Imaging Integration for Diagnosis and Prognostication of Digestive System Diseases
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D004066', 'term': 'Digestive System Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 5000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-07-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-07', 'completionDateStruct': {'date': '2026-08-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-07-20', 'studyFirstSubmitDate': '2025-07-20', 'studyFirstSubmitQcDate': '2025-07-20', 'lastUpdatePostDateStruct': {'date': '2025-07-28', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-07-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-08-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The area under the ROC curve (AUC) to assess the performance of diagnostic model.', 'timeFrame': '6 months', 'description': 'After baseline MR or CT scanning, patients were followed up.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Radiology', 'Imaging', 'Digestive Diseases', 'Artificial Intelligence'], 'conditions': ['Digestive Diseases', 'Radiology', 'AI (Artificial Intelligence)', 'Imaging']}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to to develop a noninvasive disease assessment system by leveraging artificial intelligence (AI) to comprehensively analyze multi-modal imaging features, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), for the diagnosis and prognostication of digestive diseases.\n\nParticipants will be randomly assigned to either conventional endoscopy or virtual endoscopy groups. The predictive performance of both groups for prognostic indicators, such as clinical remission rate and recurrence risk, will be compared during follow-up to verify the non-inferiority of the virtual endoscopy group.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'This study included patients with digestive diseases who were admitted to the First Affiliated Hospital of Sun Yat-sen University between July 2025 and August 2026.', 'healthyVolunteers': True, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Patients with multimodal-confirmed diagnoses (clinical, imaging, endoscopic, and pathological) of:\n\n * Inflammatory bowel disease (IBD; Crohn's disease or ulcerative colitis)\n * Intestinal tuberculosis\n * Behçet's disease\n* Availability of ≥1 technically adequate CT or MR scan with high-quality colonoscopy performed within ±1 month of imaging.\n\nExclusion Criteria:\n\n* ・Suboptimal imaging quality (e.g., low-dose artifacts, metal artifacts)\n\n * Inadequate bowel preparation for endoscopy\n * Incomplete examinations due to poor tolerance"}, 'identificationModule': {'nctId': 'NCT07087418', 'briefTitle': 'AI-Driven Multimodal Imaging Integration for Diagnosis and Prognostication of Digestive System Diseases', 'organization': {'class': 'OTHER', 'fullName': 'First Affiliated Hospital, Sun Yat-Sen University'}, 'officialTitle': 'AI-Driven Multimodal Imaging Integration for Diagnosis and Prognostication of Digestive System Diseases', 'orgStudyIdInfo': {'id': '82270693'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Conventional endoscopy group', 'description': 'Prognostic indicators (e.g., clinical remission rate, recurrence risk) were predicted using conventional endoscopy at baseline.', 'interventionNames': ['Diagnostic Test: Kaplan-Meier analysis with log-rank testing']}, {'label': 'Virtual endoscopy group', 'description': 'Prognostic indicators were predicted using virtual endoscopy at baseline.', 'interventionNames': ['Diagnostic Test: Kaplan-Meier analysis with log-rank testing']}], 'interventions': [{'name': 'Kaplan-Meier analysis with log-rank testing', 'type': 'DIAGNOSTIC_TEST', 'description': 'The non-inferiority of the virtual endoscopy group will be verified by comparing the predictive performance for prognostic indicators (e.g., clinical remission rate, recurrence risk) between the two groups during follow-up.', 'armGroupLabels': ['Conventional endoscopy group', 'Virtual endoscopy group']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Shanghai', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Xuehua Li', 'role': 'CONTACT', 'email': 'lxueh@mail.sysu.edu.cn', 'phone': '13580364103'}], 'facility': 'XploreMET v3.0 system', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Xuehua Li', 'role': 'CONTACT', 'email': 'lxueh@mail.sysu.edu.cn', 'phone': '13580364103'}, {'name': 'Yaoqi Ke', 'role': 'CONTACT', 'email': 'keyq3@mail2.sysu.edu.cn', 'phone': '18316712708'}], 'overallOfficials': [{'name': 'Xuehua Li', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Sun Yat-sen University First Affiliated Hospital Department of Radiology'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'First Affiliated Hospital, Sun Yat-Sen University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'associate chief physician', 'investigatorFullName': 'Xuehua Li', 'investigatorAffiliation': 'First Affiliated Hospital, Sun Yat-Sen University'}}}}