Ignite Creation Date:
2026-03-26 @ 3:16 PM
Ignite Modification Date:
2026-03-30 @ 7:40 PM
Study NCT ID:
NCT07401368
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-10
First Post:
2026-01-23
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Clinicians' Trust in AI-Based Fetal Growth Estimates
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': ['PARTICIPANT'], 'maskingDescription': 'Participants are unaware of their allocation to the control or intervention group and are not informed that different versions of the questionnaire exist.'}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'Participants are randomized to either an intervention group receiving information about AI model performance or a control group without such information. Groups are assessed in parallel.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 308}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-06-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2028-12-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-03', 'studyFirstSubmitDate': '2026-01-23', 'studyFirstSubmitQcDate': '2026-02-03', 'lastUpdatePostDateStruct': {'date': '2026-02-10', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-10', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-12-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': "Clinicians' choice of fetal weight estimation method", 'timeFrame': 'Immediately after questionnaire completion', 'description': 'The proportion of cases in which clinicians choose the AI-based fetal weight estimate rather than the traditional Hadlock estimate when assessing anonymized ultrasound cases.'}], 'secondaryOutcomes': [{'measure': "Clinicians' confidence in selected fetal weight estimate", 'timeFrame': 'Immediately after questionnaire completion', 'description': "Clinicians' self-reported confidence in the selected fetal weight estimate, measured on a 7-point Likert scale for each case."}, {'measure': 'Recommendation of follow-up growth scan', 'timeFrame': 'Immediately after questionnaire completion', 'description': 'Whether clinicians recommend a follow-up fetal growth scan based on the selected fetal weight estimate, recorded as a binary outcome (yes/no).'}, {'measure': 'Impact of uncertainty information on model preference', 'timeFrame': 'Immediately after questionnaire completion', 'description': "Difference in clinicians' preference for AI-based versus traditional fetal weight estimates when AI predictions are presented with versus without uncertainty information."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Clinical decision-making', 'Artificial intelligence', 'Fetal weight estimation', 'Obstetric ultrasound', 'Trust', 'Human-AI interaction', 'Questionnaire study'], 'conditions': ['Fetal Growth', 'Obstetric Ultrasonography', 'Pregnancy', 'Clinical Decision-making']}, 'descriptionModule': {'briefSummary': "This study examines how clinicians trust and use artificial intelligence (AI) when estimating fetal weight during pregnancy.\n\nAccurate assessment of fetal growth is important for identifying growth problems that may affect pregnancy management. New AI-based tools can estimate fetal weight from ultrasound images, but little is known about how clinicians trust these estimates or how uncertainty information influences their decisions.\n\nIn this study, clinicians will review anonymized ultrasound cases and compare fetal weight estimates generated by an AI model with traditional estimates. Some clinicians will also be shown information about the AI model's performance and uncertainty, while others will not.\n\nParticipants will be asked to choose which estimate they find most reliable, indicate their level of confidence, and decide whether they would recommend follow-up scans. The study aims to better understand how AI and uncertainty information affect clinical decision-making and trust among clinicians with different levels of experience.", 'detailedDescription': "This is a randomized, matched, vignette-based questionnaire study designed to investigate clinicians' trust in and use of AI-based fetal growth estimates.\n\nClinicians from obstetrics and gynecology departments will be recruited and stratified by experience level. Participants will be randomized to either a control group or an intervention group. The intervention group will receive brief information about the AI model's overall performance, while the control group will not receive this information.\n\nEach participant will assess a set of anonymized third-trimester ultrasound cases. For each case, clinicians will be presented with standard ultrasound images and relevant clinical context. They will be shown fetal weight estimates generated by an AI-based model and by a traditional biometric method, with or without accompanying uncertainty information in the form of confidence intervals.\n\nFor each case, clinicians will select the estimate they consider most clinically reliable, rate their confidence in that choice, and indicate whether they would recommend a follow-up growth scan. Case sets are matched by clinical experience, ensuring that identical cases are evaluated by clinicians with similar backgrounds across study arms.\n\nThe study focuses on clinicians as participants and involves no patient intervention. All ultrasound data are fully anonymized. The results will provide insight into how AI-generated estimates and uncertainty information influence clinical trust, preferences, and decision-making in fetal growth assessment."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Clinicians working in obstetrics and gynecology departments.\n* Regular use of obstetric ultrasound in clinical practice.\n* Willingness to participate in a questionnaire-based study.\n\nExclusion Criteria:\n\n* Clinicians who do not perform obstetric ultrasound examinations.\n* Clinicians with a known conflict of interest related to the AI system being evaluated.'}, 'identificationModule': {'nctId': 'NCT07401368', 'briefTitle': "Clinicians' Trust in AI-Based Fetal Growth Estimates", 'organization': {'class': 'OTHER', 'fullName': 'Rigshospitalet, Denmark'}, 'officialTitle': "Clinicians' Trust and Decision-Making Using AI-Based Fetal Growth Estimates With and Without Uncertainty: A Randomized Questionnaire Study", 'orgStudyIdInfo': {'id': 'F-25022462'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Control - No AI Performance Information', 'description': "Participants complete the questionnaire without receiving information about the AI model's overall performance."}, {'type': 'OTHER', 'label': 'ntervention - AI Performance Information', 'description': "Participants receive brief information about the AI model's overall performance before completing the questionnaire.", 'interventionNames': ['Other: Intervention - AI Performance Information']}], 'interventions': [{'name': 'Intervention - AI Performance Information', 'type': 'OTHER', 'description': "Participants receive brief information about the AI model's overall performance before completing the questionnaire.", 'armGroupLabels': ['ntervention - AI Performance Information']}]}, 'contactsLocationsModule': {'locations': [{'zip': '4200', 'city': 'Slagelse', 'country': 'Denmark', 'contacts': [{'name': 'Zahra Bashir, MD', 'role': 'CONTACT', 'email': 'zab@regsj.dk', 'phone': '+45 58 55 37 05'}], 'facility': 'Department of Obstetrics and Gynecology, Slagelse Hospital', 'geoPoint': {'lat': 55.40276, 'lon': 11.35459}}], 'centralContacts': [{'name': 'Zahra Bashir, MD', 'role': 'CONTACT', 'email': 'zab@regsj.dk', 'phone': '004574871407'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Rigshospitalet, Denmark', 'class': 'OTHER'}, 'collaborators': [{'name': 'Slagelse Hospital', 'class': 'OTHER'}, {'name': 'Copenhagen Academy for Medical Education and Simulation', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Dr.', 'investigatorFullName': 'Zahra Bashir', 'investigatorAffiliation': 'Rigshospitalet, Denmark'}}}}