This study develops an end-to-end Vision Transformer (ViT)-based artificial intelligence system for ultrasound-based diagnosis of placenta accreta spectrum (PAS), aiming to improve the accuracy and efficiency of prenatal screening using standardized ultrasound video inputs.

Placenta accreta spectrum (PAS) is a life-threatening obstetric disorder involving abnormal placental invasion into the uterine wall, which is associated with severe maternal and neonatal complications. Despite advances in imaging, prenatal diagnosis remains challenging due to variability in ultrasound interpretation and reliance on operator expertise.

This study will establish a standardized ultrasound video acquisition protocol and develop a deep learning-based model using Vision Transformer (ViT) architecture to process dynamic ultrasound sequences. The model will be trained using clinically confirmed postpartum outcomes as reference labels.

The diagnostic performance of the system will be systematically evaluated, with the goal of improving consistency in interpretation and supporting more efficient clinical decision-making in prenatal PAS screening.

Inclusion criteria:

1. Pregnant women aged between 18 and 45 years;
2. Gestational age between 24 and 34 weeks;
3. Pregnant women with a history of placenta previa;
4. Pregnant women with an anterior placenta;
5. Willingness to participate in the study and provision of written informed consent.

Exclusion criteria:

1. Failure to provide written informed consent;
2. Presence of severe complications that precluded continuation of pregnancy;
3. Inability to comply with study procedures for other reasons.

Pregnant women aged 18-45 years; Gestational age between 24 and 34 weeks; Pregnant women with a history of placenta previa or with an anterior placenta.