Fetal Growth AbnormalitiesNCT07476638
Impact of AI Feedback on Ultrasound Biometry Accuracy Across the Expertise Levels
A study testing AI interventional group in people with Fetal Growth Abnormalities and Fetal Weight, run by Copenhagen Academy for Medical Education and Simulation at 3 sites in Denmark.
Trial summary
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). Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Fetal Growth Abnormalities, Fetal Weight
- What is being tested?
- AI interventional group
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 75 participants
- Where
- 3 sitesDenmark
- Study dates
- 1 Mar 2026to 1 Mar 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Pre pregnancy BMI < 40
- Singelton pregnancy
- GA ≥ 37+0 at time of induction
- Intact membranes (to ensure consistent amniotic fluid index)
Who cannot join
- If the participants do not understand and speak either Danish or English
- Pregnant women:
- Major fetal anatomical anomaly
- Anhydramnios (DVP < 2 cm)
- CPR ratio < 2.5th percentile
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Denmark3 sites, 3 recruiting
- Enrolling by invitation
Rigshospitalet
Copenhagen, København Ø, 2100
- Recruiting
Rigshospitalet
Copenhagen, København Ø, 2100
Martin Tolsgaard, professor,, Medical doctor, Ph.D, +4520773779, martin.groennebaek.tolsgaard@regionh.dk
- Not yet recruiting
Nordsjællands Hospital
Hillerød, Capital Region, 3400
Gitte Hedermann, Associate Professor, +2520773779, gitte.hedermann.christensen@regionh.dk
About this study
Official title: Evaluating the Sensitivity to Change of AI-Feedback in Ultrasound Biometry: A Stratified Randomized Controlled Trial Across the Expertise Gradient
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).
Read the detailed description
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:
- Procedural Efficiency: Total procedure time (probe-to-skin) required to complete the biometry.
- Image Quality: Objective assessment of captured planes based on standardized salomon criteria.
- Relative Measurement Error: Deviation of estimated fetal weight when compared to a standard (expert-validated) ultrasound scan.
- Subjective Workload: Evaluation of cognitive load and user effort using the NASA Task Load Index (NASA-TLX).
- Determination of Experience Threshold: Defining the 'cutoff' in clinical experience (years and total scans) for significant AI-mediated accuracy gains.
- Conditions
- Fetal Growth Abnormalities, Fetal Weight
- Being tested
- AI interventional group
- Keywords
- Artificifial Intelligence feedback, Fetal weight estimation, Expertise reversal effect, Cognitive load, Explainable AI, Ultrasound, third trimester
- Medical subject headings
- Fetal Weight
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI interventional groupDevice
Participants in the intervention arm perform fetal biometry with the assistance of real-time Artificial Intelligence (AI) feedback software.
| Study group | Role | What they receive |
|---|---|---|
| Control Group | No intervention | Participants in the control arm perform fetal biometry using standard manual techniques without any AI assistance. |
| AI intervention Group | Experimental | 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. Receives: Device: AI interventional group |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: The two scans will be performed within a timeframe of 14 days.
Secondary outcomes (5)
Procedural Efficacy
Scan duration (seconds) will be modeled as the dependent variable to assess the tem-poral impact of the AI-feedback.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Through study completion, an average of 1 year.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Mar 2026
- Study start1 Mar 2026
- Primary completion1 Mar 2027 (estimated)
- Study completion1 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Fetal Growth AbnormalitiesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Stratified, parallel-group Randomized Controlled Trial (RCT) using a 3 x 2 factorial design
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 75 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Copenhagen Academy for Medical Education and Simulation
- Sponsor type
- University, hospital or other
- Collaborators
- Copenhagen University Hospital, Rigshospitalet, Slagelse Hospital
- Responsible party
- Principal investigator, Mary Le Ngo, Copenhagen Academy for Medical Education and Simulation
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07476638
- Sponsor's protocol ID
- F-250551591
- Status last verified
- Aug 2026
- First submitted
- 26 Feb 2026
- Last updated
- 6 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Sensitivity to Change of AI-Feedback in Ultrasound Biometry: A Stratified Randomized Controlled Trial Across the Expertise Gradient
- ClinicalTrials.gov ID
- NCT07476638
- Acronym / sponsor ID
- F-250551591
- Conditions
- Fetal Growth Abnormalities, Fetal Weight
- Keywords
- Artificifial Intelligence feedback, Fetal weight estimation, Expertise reversal effect, Cognitive load, Explainable AI, Ultrasound, third trimester
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 75 participants (estimated)
- Lead sponsor
- Copenhagen Academy for Medical Education and SimulationUniversity, hospital or other
- Collaborators
- Copenhagen University Hospital, Rigshospitalet, Slagelse Hospital
- Study start
- 1 Mar 2026 (actual)
- Primary completion
- 1 Mar 2027 (estimated)
- Study completion
- 1 Mar 2027 (estimated)
- First posted
- 17 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Detailed description
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:
- Procedural Efficiency: Total procedure time (probe-to-skin) required to complete the biometry.
- Image Quality: Objective assessment of captured planes based on standardized salomon criteria.
- Relative Measurement Error: Deviation of estimated fetal weight when compared to a standard (expert-validated) ultrasound scan.
- Subjective Workload: Evaluation of cognitive load and user effort using the NASA Task Load Index (NASA-TLX).
- Determination of Experience Threshold: Defining the 'cutoff' in clinical experience (years and total scans) for significant AI-mediated accuracy gains.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
- Model description
- Stratified, parallel-group Randomized Controlled Trial (RCT) using a 3 x 2 factorial design
| Arm | Type | Receives |
|---|---|---|
| Control Group | No intervention | –Participants in the control arm perform fetal biometry using standard manual techniques without any AI assistance. |
| AI intervention Group | Experimental | Device: AI interventional groupThe 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (4 listed)
- Pre pregnancy BMI < 40
- Singelton pregnancy
- GA ≥ 37+0 at time of induction
- Intact membranes (to ensure consistent amniotic fluid index)
Key exclusion criteria (5 listed)
- If the participants do not understand and speak either Danish or English
- Major fetal anatomical anomaly
- Anhydramnios (DVP < 2 cm)
- CPR ratio < 2.5th percentile
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI interventional groupParticipants in the intervention arm perform fetal biometry with the assistance of real-time Artificial Intelligence (AI) feedback software. | Device | AI intervention Group |
Primary outcome
To evaluate the sensitivity to change in ultrasound measurement accuracy when using AI-feedback compared to standard scanning
Time frame: The two scans will be performed within a timeframe of 14 days.
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.
5 secondary outcomes
Procedural Efficacy
Time frame: The duration of the scan, maximum of 30 minutes
Image Quality
Time frame: Through study completion, an average of 1 year.
Cognitive and Physiological Load
Time frame: During the ultrasound procedure (GSR) and immediately following the procedure (NASA-TLX), approximately 30 minutes in total.
Measurement Deviation:
Time frame: The duration from pre study scan and study scan.
Experience Threshold for AI-Mediated Accuracy Gains
Time frame: Through study completion, an average of 1 year.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Aug 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI interventional group in people with Fetal Growth Abnormalities and Fetal Weight, run by Copenhagen Academy for Medical Education and Simulation at 3 sites in Denmark.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 6 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Denmark. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mary Le Ngo, Medical Doctor (MD), PhD stude (+45 20773779, mary.van.anh.le.ngo.01@regionh.dk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To evaluate the sensitivity to change in ultrasound measurement accuracy when using AI-feedback compared to standard scanning (time frame: The two scans will be performed within a timeframe of 14 days.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Aug 2026.