BIOmetric MEasurements in Diagnostics: Comparison of EXperts and IA-assisted Residents (BIOMEDEXIA)

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 Overview

• Status: Completed
• Conditions: Pregnancy
• Intervention / Treatment: Other: standard biometric ultrasound

• Study Type: Observational
• Enrollment (Actual): 60

Contacts and Locations

Study Locations

• France
  • Lyon, France, 69004
    • Hospices Civils de Lyon, Maternité Croix Rousse

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Pregnant woman

Description

Inclusion Criteria:

• 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).

Exclusion Criteria:

• 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)

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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.

Other: standard biometric ultrasound; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	36 weeks amenorrhea

Collaborators and Investigators

• Sponsor: Hospices Civils de Lyon

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2025
• Primary Completion (Actual): April 1, 2025
• Study Completion (Actual): April 1, 2025

Study Registration Dates

• First Submitted: March 12, 2025
• First Submitted That Met QC Criteria: March 18, 2025
• First Posted (Actual): March 24, 2025

Study Record Updates

• Last Update Posted (Actual): April 25, 2025
• Last Update Submitted That Met QC Criteria: April 22, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Keywords: ultrasound; artificial intelligence; obstetrics; Biometric measurements
• Other Study ID Numbers: CRC_GHN_2025_001

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No