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Study for the classification of uterine tumor using ultrasonography and deep learning

Study for the classification of uterine tumor using ultrasonography and deep learning

Status
Recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR1800017554
Enrollment
Unknown
Registered
2018-08-03
Start date
2018-08-01
Completion date
Unknown
Last updated
2019-05-13

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

Conditions

Uterine Tumor

Interventions

Gold Standard:color doppler ultrasound or MRI
Index test:computer&#32
assistant&#32
diagnosis

Sponsors

Tongji Hospital of Huazhong University of Science and Technology
Lead Sponsor

Eligibility

Sex/Gender
Female

Inclusion criteria

Inclusion criteria: 1. Clinical and pathological diagnosis of endometrial polyps, endometrial hyperplasia, endometrial carcinoma, uterine leiomyoma, adenomyosis (limited type) and uterine sarcoma; 2. Color Doppler ultrasound imaging should have good image quality.

Exclusion criteria

Exclusion criteria: Poor color doppler ultrasound images

Design outcomes

Primary

MeasureTime frame
SPE, SEN, ACC, ROC;Accuracy Rate;—

Countries

Japan

Contacts

Public ContactWang Shixuan

Huazhong University of Science and Technology

shixuanwang@126.com+86 13995553319

Outcome results

None listed

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