Evaluation of deep learning model for enhancing surgeons' intraoperative organ recognition ability

Evaluation of deep learning model for enhancing surgeons' intraoperative organ recognition ability - Intraoperative organ recognition ability enhancement evaluation test

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000058207

Enrollment

Registered

2025-06-18

Start date

2022-03-01

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

Uterine fibroids, Adenomyosis, uterine cancer

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Gynecologists who meet any of the following criteria will be included in this study. 1) Non-specialists: Obstetricians and gynecologists who do not meet the criteria in 2) 2) Specialists: Obstetricians and gynecologists who are certified by the Japan Society of Obstetrics and Gynecology and have at least five years of clinical experience in gynecology, including initial clinical training

Exclusion criteria

Exclusion criteria: Physicians who supervised the correct answers will be excluded from this test. Obstetricians and gynecologists deemed inappropriate by the principal investigator

Design outcomes

Primary

Measure	Time frame
Sensitivity and specificity of organ recognition tests by physicians with and without AI support	—

Secondary

Measure	Time frame
Sensitivity and specificity of organ recognition tests by physicians with and without AI support according to surgical skill level	—

Countries

Japan

Contacts

Public ContactShin Takenaka

National Cancer Center Hospital East Gynecology

stakenak@east.ncc.go.jp04-7133-1111

Outcome results