Ignite Creation Date:
2025-12-24 @ 7:49 PM
Ignite Modification Date:
2025-12-25 @ 5:26 PM
Study NCT ID:
NCT06456203
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-13
First Post:
2024-06-04
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Trial of Artificial Intelligence for Chest Radiography
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D011014', 'term': 'Pneumonia'}, {'id': 'D008175', 'term': 'Lung Neoplasms'}], 'ancestors': [{'id': 'D012141', 'term': 'Respiratory Tract Infections'}, {'id': 'D007239', 'term': 'Infections'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 10000}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2024-10', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-06', 'completionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-06-11', 'studyFirstSubmitDate': '2024-06-04', 'studyFirstSubmitQcDate': '2024-06-11', 'lastUpdatePostDateStruct': {'date': '2024-06-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-06-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-09', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Report generation time', 'timeFrame': '12 months', 'description': 'Time for radiologist to produce each individual CXR report'}, {'measure': 'Turnaround Time', 'timeFrame': '12 months', 'description': 'Time from patient arrival at radiography department to time for clinical team to receive report'}, {'measure': 'Time to discharge', 'timeFrame': '12 months', 'description': 'Time from patient arrival at radiography department to time to discharge from hospital'}], 'secondaryOutcomes': [{'measure': '30-day patient readmission rate', 'timeFrame': '12 months', 'description': 'Rate of readmission of patient to hospital after discharge within 30 days'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Radiology', 'artificial intelligence', 'triage', 'radiography'], 'conditions': ['Pneumonia', 'Lung Cancer']}, 'descriptionModule': {'briefSummary': 'Randomized Clinical Trial of the impact of Chest radiograph AI-assisted triage and report generation upon clinical outcomes and an economic analysis of impact of AI decision support on radiology service delivery.', 'detailedDescription': 'Randomized, prospective selection of patients. Control group involves radiologists reporting chest radiographs as per reference standard clinical workflow Intervention group involves radiologists assisted with AI reporting an AI-triaged worklist of chest radiographs using an AI report generation tool Clinical outcomes on patients are studied at pre-determined study endpoints, including time to discharge from the hospital and re-admission rates.\n\nEconomic analysis on cost-avoidance from man-hours saved from report generation and triage.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'maximumAge': '130 Years', 'minimumAge': '14 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* All patients attending radiography to have chest radiographs during the study period\n\nExclusion Criteria:\n\n* age below 14\n* deceased before discharge\n* chest radiograph performed in non-standard projections'}, 'identificationModule': {'nctId': 'NCT06456203', 'acronym': 'ACER', 'briefTitle': 'Trial of Artificial Intelligence for Chest Radiography', 'organization': {'class': 'OTHER', 'fullName': 'Duke-NUS Graduate Medical School'}, 'officialTitle': 'Artificial Intelligence for Chest Radiography: Impact on Economics, Patient Outcomes and Radiology Service Delivery', 'orgStudyIdInfo': {'id': '2023/2280'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Control Arm', 'description': 'Chest radiographs reported with AI assistance'}, {'type': 'ACTIVE_COMPARATOR', 'label': 'AI assisted', 'description': 'AI assisted detection, triage and reporting of CXR', 'interventionNames': ['Diagnostic Test: AI']}], 'interventions': [{'name': 'AI', 'type': 'DIAGNOSTIC_TEST', 'description': 'Artificial intelligence triage and reporting system', 'armGroupLabels': ['AI assisted']}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Duke-NUS Graduate Medical School', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Clinical Assistant Professor', 'investigatorFullName': 'Charlene Liew', 'investigatorAffiliation': 'Duke-NUS Graduate Medical School'}}}}