AI feedback on fetal weight estimates across clinician experience levels
Official title: Evaluating the Sensitivity to Change of AI-Feedback in Ultrasound Biometry: A Stratified Randomized Controlled Trial Across the Expertise Gradient
Run by Copenhagen Academy for Medical Education and Simulation · NCT07476638 · Data updated 2026-11-12
A real-time AI help is tested to see if it makes fetal weight estimates from ultrasound more accurate, especially for novices and intermediates, compared with doing it manually.
Quick facts
| Study type | Interventional |
|---|---|
| Sex | All |
| Enrollment | 75 (estimated) |
| Sponsor | Copenhagen Academy for Medical Education and Simulation Academic / other |
| Locations | 3 sites (Hillerød, Capital Region and 2 other locations) |
| Status | Recruiting |
| Started | March 1, 2026 |
| Main results expected | March 1, 2027 |
| Study ends | March 1, 2027 |
| Registry record updated | August 6, 2026 |
| Trial ID | NCT07476638 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Healthcare professionals and students in obstetrics, including medical students, residents, and senior consultants, who can communicate in Danish or English.
✕Probably not a fit
Participants with barriers to AI use or non-communicable limitations may not experience improved accuracy.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This randomized trial assigns 75 participants across three expertise levels (novice, intermediate, expert) to AI-assisted or manual ultrasound biometry. The primary outcome is the accuracy of estimated fetal weight (MAPE) compared to birthweight. Secondary outcomes include procedure time, image quality, error from baseline scans, and cognitive workload measured by NASA-TLX. The study probes the Expertise Reversal Effect to see if AI benefits diminish with higher user experience. Real-time AI provides automated plane validation and calipers to the ultrasound workflow.
Why it matters
Potential benefit: If successful, AI feedback could improve fetal weight estimation accuracy and efficiency, potentially guiding better clinical decisions.
How similar studies have gone: Prior work suggests AI-assisted imaging can improve measurements, but the reversal of benefit by experience level is a novel aspect in this context.
Who can join: the full rules
Show full inclusion / exclusion criteria
Clinical Target Population: Healthcare professionals and students, including but not limited to: * Medical students (doing their masters. * Resident physicians and Senior Consultants in Obstetrics and Gynecology. Exclusion: \- If the participants do not understand and speak either Danish or English Pregnant women: Inclusion Criteria: * Pre pregnancy BMI \< 40 * Singelton pregnancy * GA ≥ 37+0 at time of induction * Intact membranes (to ensure consistent amniotic fluid index) Exclusion Criteria: * Major fetal anatomical anomaly * Anhydramnios (DVP \< 2 cm) * CPR ratio \< 2.5th percentile
Where it's running
-
Rigshospitalet
Copenhagen, København Ø, Denmark Recruiting -
Nordsjællands Hospital
Hillerød, Capital Region, Denmark Not yet recruiting -
Rigshospitalet
Copenhagen, København Ø, Denmark Enrolling by invitation
Study contacts
- Study coordinator: Mary Le Ngo, Medical Doctor (MD), PhD stude
- Email: mary.van.anh.le.ngo.01@regionh.dk
- Phone: +45 20773779
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.