Archival hysteroscopic image-based prediction for histopathologic chronic endometritis in infertile women using deep learning model

Archival hysteroscopic image-based prediction for histopathologic chronic endometritis in infertile women using deep learning model - ARChival Hysteroscopic Image-based Prediction for histopathologic chronic Endometritis in infertile women using deep LeArninG mOdel (ARCHIPELAGO Study)

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000047385

Enrollment

2000

Registered

2022-04-04

Start date

2022-04-04

Completion date

Unknown

Last updated

2026-06-29

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

Conditions

Chronic endometritis

Interventions

None listed

Eligibility

Sex/Gender

Female

Inclusion criteria

Inclusion criteria: Infertile women undergoing hysteroscopy and endometrial biopsy/histopathologic examinations in search of chronic endometritis.

Exclusion criteria

Exclusion criteria: Other women.

Design outcomes

Primary

Measure	Time frame
To train the originally developed deep learning model using archival hysteroscopic and histopathologic images of infertile women and evaluate the feasibility to clinical diagnosis of chronic endometritis.	—

Countries

Japan

Contacts

Public ContactKotaro Kitaya

Kouseikai Mihara Hospital Infertility Center

kitaya@koto.kpu-m.ac.jp+81-75-392-3111

Outcome results