Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-03-31 @ 7:40 AM
Study NCT ID:
NCT07408167
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-13
First Post:
2026-02-06
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI Models in Clinical Pathology Diagnosis: A Multicenter RCT
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'OTHER', 'interventionModel': 'CROSSOVER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2060}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-03-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2029-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-06', 'studyFirstSubmitDate': '2026-02-06', 'studyFirstSubmitQcDate': '2026-02-06', 'lastUpdatePostDateStruct': {'date': '2026-02-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2028-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Area under ROC curve (AUC)', 'timeFrame': "Assessments will be conducted within one week after the pathologists' diagnoses", 'description': 'Area under the curve'}], 'secondaryOutcomes': [{'measure': 'Diagnostic time per case', 'timeFrame': "Measured immediately after the pathologists' diagnosis", 'description': 'Time required for the pathologist to complete the diagnosis of each case in the AI-assisted diagnosis group compared with the independent diagnosis group. Diagnostic time is defined as the duration (in minutes/seconds) from initiating case review to finalizing and submitting the diagnostic report in the study system.'}, {'measure': "Pathologists' diagnostic confidence", 'timeFrame': 'At the time of diagnosis for each case.', 'description': 'Self-reported diagnostic confidence of pathologists for each case in the AI-assisted diagnosis group compared with the independent diagnosis group. Diagnostic confidence will be rated by the reporting pathologist on a \\[10\\]-point Likert scale (e.g., 1 = very uncertain to 10 = very confident) immediately after completing the diagnosis. Higher scores indicate greater diagnostic confidence.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Randomized Controlled Trial'], 'conditions': ['Pathology Foundation Model']}, 'descriptionModule': {'briefSummary': "The investigators plan to conduct a multicenter, prospective, randomized controlled trial to systematically evaluate the incremental value of pathology-based artificial intelligence (AI) models in a pan-disease diagnostic workflow. The study will primarily compare interpretation using an AI-assisted platform with conventional independent slide reading in terms of diagnostic accuracy (e.g., AUC), reading efficiency (e.g., diagnostic time), diagnostic report quality, diagnostic confidence (Likert scale), and pathologists' satisfaction with the AI model. Investigators will also assess superiority among less experienced (junior) pathologists and non-inferiority among more experienced (senior) pathologists. Successful completion of this project will provide high-level prospective evidence to support standardized deployment, quality control, and broader implementation of pathology AI in clinical practice. This trial may also evaluate the potential benefits and risks of using AI tools in medical research.", 'detailedDescription': 'In this study, investigators plan to enroll 60 pathologists with varying levels of experience and 2,000 patients requiring pathological diagnosis, with whole-slide images (WSIs) collected.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '100 Years', 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Pathologists:\n\nInclusion Criteria:\n\n1. Voluntarily provide written informed consent.\n2. Age ≥ 20 years.\n3. Have completed at least 1 year of training in pathological diagnosis.\n\nExclusion Criteria:\n\n1. Individuals with reading difficulties or a reading disorder.\n2. Unwilling to participate in this study.\n\nPatients:\n\nInclusion Criteria:\n\n1. Voluntarily provide written informed consent.\n2. Age ≥ 18 years.\n3. Have available digital pathology images and relevant clinical information.\n\nExclusion Criteria:\n\n1. Missing data or data quality not meeting the requirements for analysis.\n2. Deemed unsuitable for participation by the investigator.\n3. Unwilling to participate in this study.'}, 'identificationModule': {'nctId': 'NCT07408167', 'briefTitle': 'AI Models in Clinical Pathology Diagnosis: A Multicenter RCT', 'organization': {'class': 'OTHER', 'fullName': 'Nanfang Hospital, Southern Medical University'}, 'officialTitle': 'Performance of AI Models in the Clinical Pathology Diagnostic Workflow: A Multicenter, Prospective, Randomized Controlled Trial', 'orgStudyIdInfo': {'id': 'NFEC-2026-059'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'AI-assisted group', 'description': 'Pathologists in this group are required to use the AI pathology diagnostic model to assist their diagnoses. The AI pathology model will provide a predicted result for each case.', 'interventionNames': ['Other: AI model']}, {'type': 'PLACEBO_COMPARATOR', 'label': 'Independent Diagnosis Group (Control Group)', 'description': 'In this group, pathologists will independently diagnose each case based on their own clinical experience, and will record both their time to diagnosis and their diagnostic confidence.', 'interventionNames': ['Other: Control']}], 'interventions': [{'name': 'AI model', 'type': 'OTHER', 'description': 'Doctors in this group are required to use the AI model to assist their diagnoses. The AI pathology model will provide a predicted result for each case.', 'armGroupLabels': ['AI-assisted group']}, {'name': 'Control', 'type': 'OTHER', 'description': 'Pathologists will independently diagnose each case based on their own clinical experience, and will record both their time to diagnosis and their diagnostic confidence.', 'armGroupLabels': ['Independent Diagnosis Group (Control Group)']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Chongqing', 'state': 'Chongqing Municipality', 'country': 'China', 'contacts': [{'name': 'Xiaohong Yao', 'role': 'CONTACT', 'email': 'yxh15@163.com', 'phone': '17720466296'}], 'facility': 'The First Hospital Affiliated to AMU SOUTHWEST HOSPITAL', 'geoPoint': {'lat': 29.56026, 'lon': 106.55771}}, {'zip': 'China 510515', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'contacts': [{'name': 'Li Liang', 'role': 'CONTACT', 'email': 'lli@smu.edu.cn', 'phone': '+8615989146266'}, {'name': 'Zhengyu Zhang', 'role': 'CONTACT', 'email': 'zzyusmu@163.com'}], 'facility': 'Nanfang Hospital, Southern Medical University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'city': 'Zhengzhou', 'state': 'Henan', 'country': 'China', 'contacts': [{'name': 'Shenglei Li', 'role': 'CONTACT', 'email': '13598423254@126.com', 'phone': '13598423254'}], 'facility': 'The First Affiliated Hospital of Zhengzhou University', 'geoPoint': {'lat': 34.75778, 'lon': 113.64861}}], 'centralContacts': [{'name': 'Zhengyu Zhang', 'role': 'CONTACT', 'email': 'zzyusmu@163.com', 'phone': '13837365993'}], 'overallOfficials': [{'name': 'Li Liang', 'role': 'STUDY_CHAIR', 'affiliation': 'Nanfang Hospital, Southern Medical University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Nanfang Hospital, Southern Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}