This study serves as a supplemental investigation to the randomized controlled SCAN-AID study (NCT0632187). This study will evaluate and compare the fetal growth estimation outcomes of AI-supported groups, expert sonographers, and control groups using a secondary AI predictive model.

The goal of this study is to compare the effects of two distinct AI methods on fetal ultrasound diagnostic accuracy. It serves as a supplementary investigation to the SCAN-AID study (NCT NCT06232187). This study aims will compare the diagnostic accuracy of two types of AI methods.

From the SCAN-AID study ultrasound novices were randomized into one of three groups with different levels of AI support: control group, AI feedback group 1 where the participants are presented with basic black box AI feedback, and AI feedback group 2 where the participants are presented with a more detailed explainable AI feedback. All the participants are tasked to perform an ultrasound fetal weight estimation (EFW) on pregnant women at gestational age 30-37. The outcomes were than compared to the expert sonographers measurements.

In this study an operator independent AI method that predicts the fetal weight is used on the SCAN-AID ultrasound examinations. .

Pregnant women:

Inclusion Criteria:

• Singelton pregnant.
• Gestational age: 30-38 weeks
• Maternal age < 40 years

Exclusion Criteria:

• Oligo hydramnion
• Severe fetal anomaly e.g. fetal heart anomaly, omphalocele etc.
• Severe fetal macrosomia or growth restriction.

Medical students are recruited from the Medical universities in Denmark (mainly Copenhagen University) Pregnant women are chosen from the fetal medical departement at Rigshospitalet, Copenhagen.