FindTrial
Sign In

Deep Learning-based Ultrasound Image and Video Segmentation Methods for Cesarean Scar Defect

Deep Learning-based Ultrasound Image and Video Segmentation Methods for Cesarean Scar Defect

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500096803
Enrollment
Unknown
Registered
2025-02-07
Start date
2025-02-07
Completion date
Unknown
Last updated
2025-02-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cesarean Scar Defect

Interventions

CSD-US:None

Sponsors

The international peace maternity and child health hospital
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
22 Years to 50 Years

Inclusion criteria

Inclusion criteria: 1.Patients diagnosed with CSD at our center who have undergone transvaginal ultrasound examination were included.

Exclusion criteria

Exclusion criteria: 1.Patients with poor image quality from the transvaginal ultrasound will be excluded.

Design outcomes

Primary

MeasureTime frame
The accuracy and efficiency of the developed deep learning-based method for the segmentation of ultrasound images and videos in the task of CSD segmentation.;—

Countries

China

Contacts

Public ContactJian Zhang

The international peace maternity and child health hospital

ipmch@foxmail.com+86 21 6407 0434

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026