Comparison of EXperts and IA-assisted Residents

NCT ID: NCT07133165

Last Updated: 2025-08-20

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 60 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-01-01
• Study Completion Date: 2025-03-01

Brief 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. 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

Obstetric Ultrasound Biometric Measurements With or Without IA

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

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.

standard biometric ultrasound

Intervention Type: OTHER

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.

Interventions

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.

Intervention Type: OTHER

Eligibility Criteria

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)

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 45 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Hospices Civils de Lyon

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hospices Civils de Lyon, Maternité Croix Rousse

Lyon, France, France

Countries

France

Other Identifiers

25-5050

Identifier Type: -

Identifier Source: org_study_id