Fetal GrowthNCT07401368
Clinicians' Trust in AI-Based Fetal Growth Estimates
A study testing Intervention - AI Performance Information in people with Fetal Growth and Obstetric Ultrasonography, run by Copenhagen University Hospital, Rigshospitalet at 1 site in Denmark.
Trial summary
This study examines how clinicians trust and use artificial intelligence (AI) when estimating fetal weight during pregnancy. Accurate assessment of fetal growth is important for identifying growth problems that may affect pregnancy management. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Fetal Growth, Obstetric Ultrasonography, Pregnancy
- What is being tested?
- Intervention - AI Performance Information
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 130 participants
- Where
- 1 siteDenmark
- Study dates
- 27 Apr 2026to 1 Dec 2028
- 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
- Clinicians working in obstetrics and gynecology departments.
- Regular use of obstetric ultrasound in clinical practice.
- Willingness to participate in a questionnaire-based study.
Who cannot join
- Clinicians who do not perform obstetric ultrasound examinations.
- Clinicians with a known conflict of interest related to the AI system being evaluated.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Denmark1 site
Department of Obstetrics and Gynecology, Slagelse Hospital
Slagelse, 4200
About this study
Official title: Clinicians' Trust and Decision-Making Using AI-Based Fetal Growth Estimates With and Without Uncertainty: A Randomized Questionnaire Study
This study examines how clinicians trust and use artificial intelligence (AI) when estimating fetal weight during pregnancy.
Accurate assessment of fetal growth is important for identifying growth problems that may affect pregnancy management. New AI-based tools can estimate fetal weight from ultrasound images, but little is known about how clinicians trust these estimates or how uncertainty information influences their decisions.
Read more
In this study, clinicians will review anonymized ultrasound cases and compare fetal weight estimates generated by an AI model with traditional estimates. Some clinicians will also be shown information about the AI model's performance and uncertainty, while others will not.
Participants will be asked to choose which estimate they find most reliable, indicate their level of confidence, and decide whether they would recommend follow-up scans. The study aims to better understand how AI and uncertainty information affect clinical decision-making and trust among clinicians with different levels of experience.
Read the detailed description
This is a randomized, matched, vignette-based questionnaire study designed to investigate clinicians' trust in and use of AI-based fetal growth estimates.
Clinicians from obstetrics and gynecology departments will be recruited and stratified by experience level. Participants will be randomized to either a control group or an intervention group. The intervention group will receive brief information about the AI model's overall performance, while the control group will not receive this information.
Each participant will assess a set of anonymized third-trimester ultrasound cases. For each case, clinicians will be presented with standard ultrasound images and relevant clinical context. They will be shown fetal weight estimates generated by an AI-based model and by a traditional biometric method, with or without accompanying uncertainty information in the form of confidence intervals.
For each case, clinicians will select the estimate they consider most clinically reliable, rate their confidence in that choice, and indicate whether they would recommend a follow-up growth scan. Case sets are matched by clinical experience, ensuring that identical cases are evaluated by clinicians with similar backgrounds across study arms.
The study focuses on clinicians as participants and involves no patient intervention. All ultrasound data are fully anonymized. The results will provide insight into how AI-generated estimates and uncertainty information influence clinical trust, preferences, and decision-making in fetal growth assessment.
- Being tested
- Intervention - AI Performance Information
- Keywords
- Clinical decision-making, Artificial intelligence, Fetal weight estimation, Obstetric ultrasound, Trust, Human-AI interaction, Questionnaire study
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Intervention - AI Performance InformationOther
Participants receive brief information about the AI model's overall performance before completing the questionnaire.
| Study group | Role | What they receive |
|---|---|---|
| Control - No AI Performance Information | No intervention | Participants complete the questionnaire without receiving information about the AI model's overall performance. |
| ntervention - AI Performance Information | Other | Participants receive brief information about the AI model's overall performance before completing the questionnaire. Receives: Other: Intervention - AI Performance Information |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinicians' choice of fetal weight estimation method
The proportion of cases in which clinicians choose the AI-based fetal weight estimate rather than the traditional Hadlock estimate when assessing anonymized ultrasound cases.
Measured: Immediately after questionnaire completion
Secondary outcomes (3)
Clinicians' confidence in selected fetal weight estimate
Clinicians' self-reported confidence in the selected fetal weight estimate, measured on a 7-point Likert scale for each case.
Measured: Immediately after questionnaire completion
Recommendation of follow-up growth scan
Whether clinicians recommend a follow-up fetal growth scan based on the selected fetal weight estimate, recorded as a binary outcome (yes/no).
Measured: Immediately after questionnaire completion
Impact of uncertainty information on model preference
Difference in clinicians' preference for AI-based versus traditional fetal weight estimates when AI predictions are presented with versus without uncertainty information.
Measured: Immediately after questionnaire completion
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Feb 2026
- Study start27 Apr 2026
- Primary completion1 Dec 2027 (estimated)
- Study completion1 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Fetal GrowthStudies of the same condition, open and under way.
- More trials run by Copenhagen University Hospital, RigshospitaletThe sponsor's other registered studies.
- 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Participants are randomized to either an intervention group receiving information about AI model performance or a control group without such information. Groups are assessed in parallel.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 130 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Copenhagen University Hospital, Rigshospitalet
- Sponsor type
- University, hospital or other
- Collaborators
- Slagelse Hospital, Copenhagen Academy for Medical Education and Simulation
- Responsible party
- Principal investigator, Zahra Bashir, Copenhagen University Hospital, Rigshospitalet
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07401368
- Sponsor's protocol ID
- F-25022462
- Status last verified
- Aug 2026
- First submitted
- 23 Jan 2026
- Last updated
- 5 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinicians' Trust and Decision-Making Using AI-Based Fetal Growth Estimates With and Without Uncertainty: A Randomized Questionnaire Study
- ClinicalTrials.gov ID
- NCT07401368
- Acronym / sponsor ID
- F-25022462
- Keywords
- Clinical decision-making, Artificial intelligence, Fetal weight estimation, Obstetric ultrasound, Trust, Human-AI interaction, Questionnaire study
- Study type
- Interventional
- Recruitment status
- Enrolling by invitation
- Enrolment
- 130 participants (estimated)
- Lead sponsor
- Copenhagen University Hospital, RigshospitaletUniversity, hospital or other
- Collaborators
- Slagelse Hospital, Copenhagen Academy for Medical Education and Simulation
- Study start
- 27 Apr 2026 (actual)
- Primary completion
- 1 Dec 2027 (estimated)
- Study completion
- 1 Dec 2028 (estimated)
- First posted
- 10 Feb 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.
This study examines how clinicians trust and use artificial intelligence (AI) when estimating fetal weight during pregnancy.
Accurate assessment of fetal growth is important for identifying growth problems that may affect pregnancy management. New AI-based tools can estimate fetal weight from ultrasound images, but little is known about how clinicians trust these estimates or how uncertainty information influences their decisions.
In this study, clinicians will review anonymized ultrasound cases and compare fetal weight estimates generated by an AI model with traditional estimates. Some clinicians will also be shown information about the AI model's performance and uncertainty, while others will not.
Participants will be asked to choose which estimate they find most reliable, indicate their level of confidence, and decide whether they would recommend follow-up scans. The study aims to better understand how AI and uncertainty information affect clinical decision-making and trust among clinicians with different levels of experience.
Detailed description
This is a randomized, matched, vignette-based questionnaire study designed to investigate clinicians' trust in and use of AI-based fetal growth estimates.
Clinicians from obstetrics and gynecology departments will be recruited and stratified by experience level. Participants will be randomized to either a control group or an intervention group. The intervention group will receive brief information about the AI model's overall performance, while the control group will not receive this information.
Each participant will assess a set of anonymized third-trimester ultrasound cases. For each case, clinicians will be presented with standard ultrasound images and relevant clinical context. They will be shown fetal weight estimates generated by an AI-based model and by a traditional biometric method, with or without accompanying uncertainty information in the form of confidence intervals.
For each case, clinicians will select the estimate they consider most clinically reliable, rate their confidence in that choice, and indicate whether they would recommend a follow-up growth scan. Case sets are matched by clinical experience, ensuring that identical cases are evaluated by clinicians with similar backgrounds across study arms.
The study focuses on clinicians as participants and involves no patient intervention. All ultrasound data are fully anonymized. The results will provide insight into how AI-generated estimates and uncertainty information influence clinical trust, preferences, and decision-making in fetal growth assessment.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- Participants are randomized to either an intervention group receiving information about AI model performance or a control group without such information. Groups are assessed in parallel.
| Arm | Type | Receives |
|---|---|---|
| Control - No AI Performance Information | No intervention | –Participants complete the questionnaire without receiving information about the AI model's overall performance. |
| ntervention - AI Performance Information | Other | Other: Intervention - AI Performance InformationParticipants receive brief information about the AI model's overall performance before completing the questionnaire. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- Clinicians working in obstetrics and gynecology departments.
- Regular use of obstetric ultrasound in clinical practice.
- Willingness to participate in a questionnaire-based study.
Key exclusion criteria (2 listed)
- Clinicians who do not perform obstetric ultrasound examinations.
- Clinicians with a known conflict of interest related to the AI system being evaluated.
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 |
|---|---|---|
| Intervention - AI Performance InformationParticipants receive brief information about the AI model's overall performance before completing the questionnaire. | Other | ntervention - AI Performance Information |
Primary outcome
Clinicians' choice of fetal weight estimation method
Time frame: Immediately after questionnaire completion
The proportion of cases in which clinicians choose the AI-based fetal weight estimate rather than the traditional Hadlock estimate when assessing anonymized ultrasound cases.
3 secondary outcomes
Clinicians' confidence in selected fetal weight estimate
Time frame: Immediately after questionnaire completion
Recommendation of follow-up growth scan
Time frame: Immediately after questionnaire completion
Impact of uncertainty information on model preference
Time frame: Immediately after questionnaire completion
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 5 Aug 2026.
- Individual participant dataSharing planned: No
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Intervention - AI Performance Information in people with Fetal Growth and Obstetric Ultrasonography, run by Copenhagen University Hospital, Rigshospitalet at 1 site 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 3 inclusion and 2 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: enrolling by invitation, as of the sponsor's last update on 5 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 1 site 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinicians' choice of fetal weight estimation method (time frame: Immediately after questionnaire completion). 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 5 Aug 2026.