PregnancyBIOMEDEXIANCT06892327
BIOmetric MEasurements in Diagnostics: Comparison of EXperts and IA-assisted Residents
An observational study of Pregnancy, run by Hospices Civils de Lyon at 1 site in France.
Trial summary
Obstetric ultrasound is the cornerstone of fetal growth assessment. It provides essential biometric measurements for estimating fetal weight, monitoring growth and identifying conditions such as intrauterine growth retardation (IUGR) or macrosomia. Read more about this study
- Who is it for?
- Ages 18 to 40 years, women only
- Conditions the study is for
- Pregnancy
- What is being tested?
- standard biometric ultrasound
- How many people
- 60 participants
- Where
- 1 siteFrance
- Study dates
- 1 Apr 2025to 1 Apr 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
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 18 to 40 years
- Women only
- No healthy volunteers
- Adult
Study population: Pregnant woman
Who may be able to join
- Pregnant women aged between 18 and 40 years.
- Singleton or twin ongoing pregnancies.
- Gestational age between 20 and 36 weeks of amenorrhea (WA).
- Patients scheduled for a biometric ultrasound (standard follow-up).
Who cannot join
- Known major fetal anomalies that could affect biometric measurements.
- Technical difficulties during the ultrasound (e.g., maternal obesity, complex abdominal scars).
- History of severe maternal conditions affecting biometric measurements (e.g., uterine malformations)
Trial locations and contacts
1 site in 1 country.
About this study
Obstetric ultrasound is the cornerstone of fetal growth assessment. It provides essential biometric measurements for estimating fetal weight, monitoring growth and identifying conditions such as intrauterine growth retardation (IUGR) or macrosomia. The accuracy of these measurements depends largely on the expertise of the operator. Experienced practitioners excel at positioning the probe, identifying anatomical landmarks and obtaining reproducible measurements. In contrast, novice operators, such as medical residents, may find it difficult to capture optimal images or identify precise landmarks, resulting in significant variability. This inter-observer variability, well documented even among experts, can have an impact on clinical decisions and obstetric management. For novices, variability is more pronounced, which can affect diagnostic reliability and patient care. Improving resident training is therefore essential to reduce this variability. Traditional solutions to minimizing variability, such as increased supervision, face limitations due to time constraints and resource availability. Recent advances in Artificial Intelligence (AI) could help in the training of residents. In obstetrics, AI could potentially automate biometric measurements by identifying key anatomical landmarks and performing precise, consistent measurements. These systems might standardize acquisition and reduce variability, making measurements less dependent on operator experience. AI technologies could significantly improve novice performance by potentially shortening the learning curve and enhancing measurement reliability. This might enable residents to work more independently while maintaining accuracy. Despite these potential advantages, few studies would have rigorously compared AI-assisted novice performance with that of expert practitioners under real-world conditions.This study aims to assess the possible effectiveness of AI in supporting novice operators during obstetric biometric measurements. The primary objective would be to determine whether AI assistance could enable novices to achieve measurement accuracy comparable to that of experienced practitioners, while potentially improving reproducibility and reducing inter-observer variability.
- Conditions
- Pregnancy
- Being tested
- standard biometric ultrasound
- Keywords
- ultrasound, Biometric measurements, artificial intelligence, obstetrics
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
standard biometric ultrasoundOther
In this study, biometric measurements were systematically performed for each patient using both manual methods and an artificial intelligence (AI) system (Live View Assist, Samsung). The AI system provided real-time guidance by identifying anatomical landmarks and assisting in the measurement of key biometric parameters, including Femur Length (FL), Abdominal Circumference (AC), Head Circumference (HC), and Biparietal Diameter (BPD). This dual approach ensured that both manual and AI-assisted methods were applied uniformly as part of routine clinical care.
| Study group | Role | What they receive |
|---|---|---|
| Routine Follow-Up: Patients scheduled for a standard biometric ultrasound. | Maternal Age: Pregnant women aged between 18 and 45 years. Pregnancy Type: Singleton viable pregnancy (excluding twin or multiple gestations).
Gestational Age: Between 17 weeks and 38 weeks of gestation. Receives: Other: standard biometric ultrasound |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of biometric measurements: Assessment of agreement between manual and AI-assisted measurements.
The individual biometric measurements, such as biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL), will be presented separately based on their respective units (cm).
The estimated fetal weight, which will be based on the aggregation of these various measurements (BPD, HC, AC, FL), will be reported in kilograms (kg). This estimate will be calculated using fetal growth formulas adapted to these parameters.
We will clarify that the fetal weight estimate will be calculated based on a model that incorporates these different measurements in the appropriate units.
In summary, each measurement will be clearly separated based on its unit, and the fetal weight estimate will be explained to show how the different measurements are combined.
Measured: 36 weeks amenorrhea
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2025
- Study start1 Apr 2025
- Primary completion1 Apr 2025
- Study completion1 Apr 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PregnancyStudies of the same condition, open and under way.
- More trials run by Hospices Civils de LyonThe 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
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 60 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hospices Civils de Lyon
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06892327
- Sponsor's protocol ID
- CRC_GHN_2025_001
- Acronym
- BIOMEDEXIA
- Status last verified
- Mar 2025
- First submitted
- 12 Mar 2025
- Last updated
- 25 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- BIOmetric MEasurements in Diagnostics: Comparison of EXperts and IA-assisted Residents
- ClinicalTrials.gov ID
- NCT06892327
- Acronym / sponsor ID
- BIOMEDEXIA / CRC_GHN_2025_001
- Conditions
- Pregnancy
- Keywords
- ultrasound, Biometric measurements, artificial intelligence, obstetrics
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 60 participants (actual)
- Lead sponsor
- Hospices Civils de LyonUniversity, hospital or other
- Study start
- 1 Apr 2025 (actual)
- Primary completion
- 1 Apr 2025 (actual)
- Study completion
- 1 Apr 2025 (actual)
- First posted
- 24 Mar 2025 (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.
Obstetric ultrasound is the cornerstone of fetal growth assessment. It provides essential biometric measurements for estimating fetal weight, monitoring growth and identifying conditions such as intrauterine growth retardation (IUGR) or macrosomia. The accuracy of these measurements depends largely on the expertise of the operator. Experienced practitioners excel at positioning the probe, identifying anatomical landmarks and obtaining reproducible measurements. In contrast, novice operators, such as medical residents, may find it difficult to capture optimal images or identify precise landmarks, resulting in significant variability. This inter-observer variability, well documented even among experts, can have an impact on clinical decisions and obstetric management. For novices, variability is more pronounced, which can affect diagnostic reliability and patient care. Improving resident training is therefore essential to reduce this variability. Traditional solutions to minimizing variability, such as increased supervision, face limitations due to time constraints and resource availability. Recent advances in Artificial Intelligence (AI) could help in the training of residents. In obstetrics, AI could potentially automate biometric measurements by identifying key anatomical landmarks and performing precise, consistent measurements. These systems might standardize acquisition and reduce variability, making measurements less dependent on operator experience. AI technologies could significantly improve novice performance by potentially shortening the learning curve and enhancing measurement reliability. This might enable residents to work more independently while maintaining accuracy. Despite these potential advantages, few studies would have rigorously compared AI-assisted novice performance with that of expert practitioners under real-world conditions.This study aims to assess the possible effectiveness of AI in supporting novice operators during obstetric biometric measurements. The primary objective would be to determine whether AI assistance could enable novices to achieve measurement accuracy comparable to that of experienced practitioners, while potentially improving reproducibility and reducing inter-observer variability.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Routine Follow-Up: Patients scheduled for a standard biometric ultrasound. | – | Other: standard biometric ultrasoundMaternal Age: Pregnant women aged between 18 and 45 years. Pregnancy Type: Singleton viable pregnancy (excluding twin or multiple gestations). Gestational Age: Between 17 weeks and 38 weeks of gestation. |
Who may qualify
- Ages
- 18 to 40 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- Study population
- Pregnant woman
Key inclusion criteria (4 listed)
- Pregnant women aged between 18 and 40 years.
- Singleton or twin ongoing pregnancies.
- Gestational age between 20 and 36 weeks of amenorrhea (WA).
- Patients scheduled for a biometric ultrasound (standard follow-up).
Key exclusion criteria (3 listed)
- Known major fetal anomalies that could affect biometric measurements.
- Technical difficulties during the ultrasound (e.g., maternal obesity, complex abdominal scars).
- History of severe maternal conditions affecting biometric measurements (e.g., uterine malformations)
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 |
|---|---|---|
| standard biometric ultrasoundIn this study, biometric measurements were systematically performed for each patient using both manual methods and an artificial intelligence (AI) system (Live View Assist, Samsung). The AI system provided real-time guidance by identifying anatomical landmarks and assisting in the measurement of key biometric parameters, including Femur Length (FL), Abdominal Circumference (AC), Head Circumference (HC), and Biparietal Diameter (BPD). This dual approach ensured that both manual and AI-assisted methods were applied uniformly as part of routine clinical care. | Other | Routine Follow-Up: Patients scheduled for a standard biometric ultrasound. |
Primary outcome
Accuracy of biometric measurements: Assessment of agreement between manual and AI-assisted measurements.
Time frame: 36 weeks amenorrhea
The individual biometric measurements, such as biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL), will be presented separately based on their respective units (cm). The estimated fetal weight, which will be based on the aggregation of these various measurements (BPD, HC, AC, FL), will be reported in kilograms (kg). This estimate will be calculated using fetal growth formulas adapted to these parameters. We will clarify that the fetal weight estimate will be calculated based on a model that incorporates these different measurements in the appropriate units. In summary, each measurement will be clearly separated based on its unit, and the fetal weight estimate will be explained to show how the different measurements are combined.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Apr 2025.
Related trials for Pregnancy
Recruiting
Methylation Profiling in Self-Samples for Identification of (Epi)Genetic Biomarkers for Gynecological Cancers
Gynaecological Cancers, Ovarian Cancer
RecruitingPhase 1/2
Study of INBRX-106 and INBRX-106 in Combination With Pembrolizumab (Keytruda®) in Subjects With Locally Advanced or Metastatic Solid Tumors (Hexavalent OX40 Agonist)
Solid Tumor, Non-Small Cell Lung Cancer
RecruitingObservational study
Population Pharmacokinetics of Temocillin in Acute Enterobacterial Pyelonephritis in Children
Pyelonephritis
Active, not recruitingPhase 3
A Study of Belzutifan (MK-6482) in Combination With Lenvatinib Versus Cabozantinib for Treatment of Renal Cell Carcinoma (MK-6482-011)
Carcinoma, Renal Cell
Recruiting
Safety and Performance of UCon Patch Electrode
Urinary Incontinence, Urinary Incontinence, Urge
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. This study is not recruiting at the moment.
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?
An observational study of Pregnancy, run by Hospices Civils de Lyon at 1 site in France.
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 3 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: finished, as of the sponsor's last update on 25 Apr 2025. 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 France. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Accuracy of biometric measurements: Assessment of agreement between manual and AI-assisted measurements. (time frame: 36 weeks amenorrhea). 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 25 Apr 2025.