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

Recruiting now · Not applicable

Conditions studied: Fetal Growth Abnormalities, Fetal Weight

In brief

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).

Key facts

Study ID

NCT07476638

Run by

Copenhagen Academy for Medical Education and Simulation

People needed

75

Starts

2026-03-01

Expected to finish

2027-03-01

Last updated by the study team

2026-08-06

Who can join

Age: any. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• Pre pregnancy BMI < 40
• Singelton pregnancy
• GA ≥ 37+0 at time of induction
• Intact membranes (to ensure consistent amniotic fluid index)

You may not qualify if…

• Major fetal anatomical anomaly
• Anhydramnios (DVP < 2 cm)
• CPR ratio < 2.5th percentile

Where it is running

• Rigshospitalet — Copenhagen, København Ø, Denmark (enrolling)
• Nordsjællands Hospital — Hillerød, Capital Region, Denmark
• Rigshospitalet — Copenhagen, København Ø, Denmark

Full record on ClinicalTrials.gov

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