An interventional study of AI interventional group in Fetal Growth Abnormalities and Fetal Weight, sponsored by Copenhagen Academy for Medical Education and Simulation. Recruiting at 3 sites in Denmark. Per ClinicalTrials.gov, last updated 2026-08-06.
Sponsored by Copenhagen Academy for Medical Education and Simulation · Not applicable, Interventional, and Diagnostic
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:
Study Overview: This study evaluates how real-time Artificial Intelligence (AI) feedback impacts the accuracy of fetal biometry measurements in obstetric ultrasound. While AI tools are designed to assist clinicians, their effectiveness may vary depending on the user's baseline skill level-a phenomenon known as the "Expertise Reversal Effect."
Research Aim: The primary objective is to determine if AI-guided feedback significantly reduces measurement error in ultrasound fetal weight estimation to traditional manual methods. The study specifically investigates whether the benefit of AI is greater for novice users, intermediate users users than for experienced specialists.
Study Design: This is a stratified, randomized controlled trial involving 75 participants categorized into three expertise tiers:
Novices (e.g., students or residents with minimal scan experience).
Intermediate Users (e.g., physicians in mid-level training).
Experts (e.g., senior specialists).
Participants within each tier will be randomized 1:1 to either the AI-Assisted Group (receiving real-time automated plane validation and calipers) or the Control Group (performing standard manual biometry).
Primary Outcome Measure: Accuracy of Estimated Fetal Weight (EFW): The Mean Absolute Percentage Error (MAPE) of the EFW relative to the actual birthweight, assessing the clinical impact of AI assistance on weight prediction.
Secondary Outcome Measures:
17 studies on the registry are indexed under Fetal Weight; 7 are open to participants now.
Browse Fetal Weight studies →Copenhagen Academy for Medical Education and Simulation is the lead sponsor of 27 studies on the registry; 6 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Clinical Target Population: Healthcare professionals and students, including but not limited to:
Exclusion:
- If the participants do not understand and speak either Danish or English
Pregnant women:
Inclusion Criteria:
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Weight in kilograms divided by height in metres squared. Example: 70 kg and 1.75 m gives 70 ÷ (1.75 × 1.75) ≈ 22.9.
What counts as overweight or obesity differs between countries (some use lower cut-offs), so go by the range the study states.
BMI (body mass index)
Weight in kilograms divided by height in metres squared. Example: 70 kg and 1.75 m gives 70 ÷ (1.75 × 1.75) ≈ 22.9.
What counts as overweight or obesity differs between countries (some use lower cut-offs), so go by the range the study states.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Participants in the control arm perform fetal biometry using standard manual techniques without any AI assistance.
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.
Device: AI interventional group
Participants in the intervention arm perform fetal biometry with the assistance of real-time Artificial Intelligence (AI) feedback software.
To evaluate the sensitivity to change in ultrasound measurement accuracy when using AI-feedback compared to standard scanning
Mean absolute percentage error (MAPE), defined as the absolute difference between estimated fetal weight (EFW) and actual birth weight (ABW) divided by actual birth weight and expressed as a percentage, for AI-assisted and manual fetal biometry.
Time frame: The two scans will be performed within a timeframe of 14 days.
Procedural Efficacy
Scan duration (seconds) will be modeled as the dependent variable to assess the tem-poral impact of the AI-feedback.
Time frame: The duration of the scan, maximum of 30 minutes
Image Quality
Salomon Quality Score on the 16-point fetal biometry plane quality scale, comparing AI-assisted and manual ultrasound acquisition. The scale ranges from 0 to 16, with higher scores indicating better anatomical plane quality.
Time frame: Through study completion, an average of 1 year.
Cognitive and Physiological Load
Both the NASA-TLX (subjective) and GSR (objective) data will be modeled as de-pendent variables. These analyses will determine if the AI-intervention significantly alters the mental effort or autonomic stress response during the procedure.
Time frame: During the ultrasound procedure (GSR) and immediately following the procedure (NASA-TLX), approximately 30 minutes in total.
Measurement Deviation:
The absolute difference between the participant's EFW and a baseline EFW performed of an experienced clinician, will be modeled to assess if AI reduces inter-observer variability.
Time frame: The duration from pre study scan and study scan.
Experience Threshold for AI-Mediated Accuracy Gains
Estimated interaction effect between operator experience (continuous, experience level) and intervention (AI-assisted vs manual) on mean absolute percentage error (MAPE), and the corresponding experience level at which the adjusted difference in MAPE between groups is not statistically significant.
Time frame: Through study completion, an average of 1 year.
PDFs hosted by the registry, ClinicalTrials.gov.
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Copenhagen Academy for Medical Education and Simulation