Comparison of EXperts and IA-assisted Residents

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.