[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"fetal-growth-abnormalities\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:fetal-growth-abnormalities":26},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":22,"briefSummary":24,"conditions":25,"keywords":28,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":38,"startDateStruct":41,"completionDateStruct":43,"leadSponsor":45,"locationsCount":5},"100629589","impact-of-ai-feedback-on-ultrasound-biometry-accuracy-across-the-expertise-levels-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":19,"type":20},75,"ESTIMATED","INTERVENTIONAL",[23],"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).",[26,27],"Fetal Growth Abnormalities","Fetal Weight",[29,30,31,32,33,34,35],"Artificifial Intelligence feedback","Fetal weight estimation","Expertise reversal effect","Cognitive load","Explainable AI","Ultrasound","third trimester","NOT_YET_RECRUITING","2026-03-26",{"date":39,"type":40},"2026-03-31","ACTUAL",{"date":42,"type":20},"2026-03-01",{"date":44,"type":20},"2027-03-01",{"name":46,"class":47},"Copenhagen Academy for Medical Education and Simulation","OTHER"]