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NCT07476638

Impact of AI Feedback on Ultrasound Biometry Accuracy Across the Expertise Levels

Not yet recruiting NA Last updated 31 March 2026
What this trial tests

NA trial testing AI interventional group in Fetal Growth Abnormalities in 75 participants. Not yet recruiting.

Timeline
1 March 2026
Primary endpoint
1 March 2027
1 March 2027

Quick facts

Lead sponsorCopenhagen Academy for Medical Education and Simulation
PhaseNA
StatusNot yet recruiting
Study typeINTERVENTIONAL
Allocationrandomized
Designparallel
Maskingsingle
Primary purposediagnostic
Enrollment75
Start date1 March 2026
Primary completion1 March 2027
Estimated completion1 March 2027
Sites1 location across Denmark

Drugs / interventions tested

Conditions studied

Sponsor

Copenhagen Academy for Medical Education and Simulation

Who can join

Eligibility, any sex, with Fetal Growth Abnormalities or Fetal Weight. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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. Design: 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. Outcomes: * Primary: EFW accuracy (MAPE) compared to actual birthweight. * Secondary: Procedure time, image quality, error relative to baseline scans, and cognitive workload (NASA-TLX).

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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Other Copenhagen Academy for Medical Education and Simulation trials

Trials by the same sponsor.

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Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07476638.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing