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Study for establishment of uterine disease detection system using hysteroscopy and deep learning

Study for establishment of uterine disease detection system using hysteroscopy and deep learning

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR1900023870
Enrollment
Unknown
Registered
2019-06-15
Start date
2019-06-10
Completion date
Unknown
Last updated
2019-06-17

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

Conditions

Uterine disease

Interventions

Gold Standard:Hysteroscopic biopsy
Index test:Computer-assisted&#32
hysteroscopic&#32
lesion&#32
localization&#32
and&#32
diagnosis&#32
classification

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 confirmed endometrial polyps, mucosal fibroids, mucosal adenomas, endometriosis (non-atypical hyperplasia and atypical hyperplasia), endometrial cancer, endometrial sarcoma); 2) Have a good quality uterine image.

Exclusion criteria

Exclusion criteria: 1) The pathological diagnosis of uterine disease is placenta remnant; 2) Accompanied by an abnormal form of the uterine cavity of the reproductive tract malformation; 3) Image quality is not good.

Design outcomes

Primary

MeasureTime frame
sensitivity;Specificity;False positive rate;Rate of missed diagnosis;—

Countries

China

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