[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100629589":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":12,"centralContacts":25,"locations":31,"responsibleParty":44,"collaborators":48,"id":53,"slug":12,"hasResults":54,"nctId":55,"briefTitle":56,"officialTitle":57,"acronym":12,"eligibilityCriteria":58,"healthyVolunteers":59,"sex":60,"minAge":12,"maxAge":12,"enrollmentInfo":61,"targetDuration":12,"studyType":64,"phases":65,"briefSummary":67,"conditions":68,"keywords":71,"overallStatus":79,"whyStopped":12,"lastUpdateSubmitDate":80,"lastUpdatePostDateStruct":81,"startDateStruct":84,"completionDateStruct":86,"leadSponsor":88,"locationsCount":89},{"fullName":5,"class":6},"Copenhagen Academy for Medical Education and Simulation","OTHER",[8,13],{"label":9,"type":10,"description":11,"interventionNames":12},"Control Group","NO_INTERVENTION","Participants in the control arm perform fetal biometry using standard manual techniques without any AI assistance.",null,{"label":14,"type":15,"description":16,"interventionNames":17},"AI intervention Group","EXPERIMENTAL","The software provides real-time \"traffic light\" or score-based feedback to validate when the correct anatomical plane (BPD, HC, AC, or FL) has been reached.",[18],"Device: AI interventional group",[20],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":12},"DEVICE","AI interventional group","Participants in the intervention arm perform fetal biometry with the assistance of real-time Artificial Intelligence (AI) feedback software.",[14],[26],{"name":27,"role":28,"phone":29,"phoneExt":12,"email":30},"Mary Le Ngo, Medical Doctor (MD), PhD stude","CONTACT","+45 20773779","mary.van.anh.le.ngo.01@regionh.dk",[32],{"facility":33,"status":12,"city":34,"state":35,"zip":36,"country":37,"cosmosGeoPoint":38,"geoPoint":43,"contacts":12},"Rigshospitalet","Copenhagen","København Ø","2100","Denmark",{"type":39,"coordinates":40},"Point",[41,42],12.56553,55.67594,{"lat":42,"lon":41},{"type":45,"investigatorFullName":46,"investigatorTitle":47,"investigatorAffiliation":5,"oldNameTitle":12,"oldOrganization":12},"PRINCIPAL_INVESTIGATOR","Mary Le Ngo","MD, PhD student",[49,51],{"name":50,"class":6},"Rigshospitalet, Denmark",{"name":52,"class":6},"Slagelse Hospital","100629589",false,"NCT07476638","Impact of AI Feedback on Ultrasound Biometry Accuracy Across the Expertise Levels","Evaluating the Sensitivity to Change of AI-Feedback in Ultrasound Biometry: A Stratified Randomized Controlled Trial Across the Expertise Gradient","Clinical Target Population: Healthcare professionals and students, including but not limited to:\n\n* Medical students (doing their masters.\n* Resident physicians and Senior Consultants in Obstetrics and Gynecology.\n\nExclusion:\n\n\\- If the participants do not understand and speak either Danish or English\n\nPregnant women:\n\nInclusion Criteria:\n\n* Pre pregnancy BMI \\\u003C 40\n* Singelton pregnancy\n* GA ≥ 37+0 at time of induction\n* Intact membranes (to ensure consistent amniotic fluid index)\n\nExclusion Criteria:\n\n* Major fetal anatomical anomaly\n* Anhydramnios (DVP \\\u003C 2 cm)\n* CPR ratio \\\u003C 2.5th percentile",true,"ALL",{"count":62,"type":63},75,"ESTIMATED","INTERVENTIONAL",[66],"NA","Objective: To evaluate the impact of real-time AI feedback on fetal biometry accuracy and investigate the Expertise Reversal Effect-whether AI benefits diminish as user experience increases.\n\nDesign: A stratified randomized trial of 75 participants (25 Novices, 25 Intermediates, 25 Experts). Users are randomized 1:1 to either AI-assisted or manual measurement groups.\n\nOutcomes:\n\n* Primary: EFW accuracy (MAPE) compared to actual birthweight.\n* Secondary: Procedure time, image quality, error relative to baseline scans, and cognitive workload (NASA-TLX).",[69,70],"Fetal Growth Abnormalities","Fetal Weight",[72,73,74,75,76,77,78],"Artificifial Intelligence feedback","Fetal weight estimation","Expertise reversal effect","Cognitive load","Explainable AI","Ultrasound","third trimester","NOT_YET_RECRUITING","2026-03-26",{"date":82,"type":83},"2026-03-31","ACTUAL",{"date":85,"type":63},"2026-03-01",{"date":87,"type":63},"2027-03-01",{"name":5,"class":6},1]