Diagnostic Trial of a Vision Transformer-Based Ultrasound AI Model for Placenta Accreta Spectrum
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Vision Transformer-Based End-to-End Ultrasound Artificial Intelligence Model.
- Who it may be relevant to
- Registry conditions: Placenta Accreta Spectrum. Basic parameters: 18 years — 45 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Diagnostic Trial of Vision Transformer-Based End-to-End Ultrasound Artificial Intelligence Model for Assisting in the Diagnosis of Placenta Accreta Spectrum Disorders: An Investigator-Initiated Prospective Clinical Study
Overview
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 test Vision 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 s
Primary outcome measures
- Diagnostic performance of the end-to-end Vision Transformer (ViT)-based ultrasound AI model for placenta accreta spectrum (PAS) [Time frame: At delivery (following confirmation of PAS status by surgical and/or pathological findings)]
Secondary outcome measures (3)
- Clinical Feasibility of the Standardized Ultrasound Video Recording Method [Time frame: At enrollment during the ultrasound examination]
- Consistency and Efficiency Between the AI Model and Physician Diagnosis [Time frame: At enrollment during the ultrasound examination]
- Safety of the AI Model [Time frame: At delivery, when PAS status and maternal outcomes are assessed]
Eligibility criteria
Inclusion criteria
- Pregnant women aged between 18 and 45 years;
- Gestational age between 24 and 34 weeks;
- Pregnant women with a history of placenta previa;
- Pregnant women with an anterior placenta;
- Willingness to participate in the study and provision of written informed consent.
Exclusion criteria
- Failure to provide written informed consent;
- Presence of severe complications that precluded continuation of pregnancy;
- Inability to comply with study procedures for other reasons.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
China · 1 center
- The Third Affiliated Hospital, Guangzhou Medical University — Guangzhou
Identifiers
NCT: NCT07643090 · Ethics Review NO. 286A01