Placenta Accreta Spectrum
Conditions
Keywords
Placenta Accreta Spectrum, Ultrasonography, Artificial Intelligence, Prenatal Diagnosis, Vision Transformer
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTVision Transformer-Based End-to-End Ultrasound Artificial Intelligence Model
An end-to-end ultrasound AI model based on the Vision Transformer (ViT) architecture was developed for the diagnosis of placenta accreta spectrum (PAS) using standardized ultrasound video inputs. Ultrasound Video Acquisition Protocol: With the patient in the supine position, the operator scanned the lower abdomen using a conventional grayscale probe. Video recording was performed in gray-scale mode for approximately 20-30 seconds, ensuring that the entire scanning region from the lower uterine segment to the uterine fundus was comprehensively captured.
Sponsors
FANG HE
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 45 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance of the end-to-end Vision Transformer (ViT)-based ultrasound AI model for placenta accreta spectrum (PAS) | At delivery (following confirmation of PAS status by surgical and/or pathological findings) | Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) of the model. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Clinical Feasibility of the Standardized Ultrasound Video Recording Method | At enrollment during the ultrasound examination | Completion Rate (Proportion of patients who successfully complete the standardized recording) and time consumption (Mean recording time) |
| Consistency and Efficiency Between the AI Model and Physician Diagnosis | At enrollment during the ultrasound examination | Diagnostic Consistency: Kappa coefficient used to evaluate the consistency between the AI model's diagnoses and those of experienced ultrasound physicians (Kappa \> 0.75 indicates good agreement). |
| Safety of the AI Model | At delivery, when PAS status and maternal outcomes are assessed | False Negative Rate: Proportion of missed PAS-positive patients and the impact on patient outcomes . |
Countries
China
Contacts
CONTACTFang He, M.D, PhD
Outcome results
None listed