UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000058207 |
|---|---|
| Receipt number | R000066548 |
| Scientific Title | Evaluation of deep learning model for enhancing surgeons' intraoperative organ recognition ability |
| Date of disclosure of the study information | 2025/06/18 |
| Last modified on | 2025/06/18 02:44:00 |
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Basic information
Public title
Evaluation of deep learning model for enhancing surgeons' intraoperative organ recognition ability
Acronym
Intraoperative organ recognition ability enhancement evaluation test
Scientific Title
Evaluation of deep learning model for enhancing surgeons' intraoperative organ recognition ability
Scientific Title:Acronym
Intraoperative organ recognition ability enhancement evaluation test
Region
| Japan |
Condition
Condition
Uterine fibroids, Adenomyosis, uterine cancer
Classification by specialty
| Obstetrics and Gynecology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
This study aimed to develop an artificial intelligence (AI) model to assist with the intraoperative recognition of the ureter and bladder and evaluate the impact of the model on physicians' organ recognition ability.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Sensitivity and specificity of organ recognition tests by physicians with and without AI support
Key secondary outcomes
Sensitivity and specificity of organ recognition tests by physicians with and without AI support according to surgical skill level
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 18 | years-old | <= |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key 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
Key exclusion criteria
Physicians who supervised the correct answers will be excluded from this test. Obstetricians and gynecologists deemed inappropriate by the principal investigator
Target sample size
16
Research contact person
Name of lead principal investigator
| 1st name | Hiroshi |
| Middle name | |
| Last name | Tanabe |
Organization
National Cancer Center Hospital East
Division name
Gynecology
Zip code
2778577
Address
6-5-1 Kashiwanoha, Kashiwa-shi, Chiba
TEL
04-7133-1111
htanabe@east.ncc.go.jp
Public contact
Name of contact person
| 1st name | Shin |
| Middle name | |
| Last name | Takenaka |
Organization
National Cancer Center Hospital East
Division name
Gynecology
Zip code
2778577
Address
6-5-1 Kashiwanoha, Kashiwa-shi, Chiba
TEL
04-7133-1111
Homepage URL
stakenak@east.ncc.go.jp
Sponsor or person
Institute
National Cancer Center
Institute
Department
Personal name
Funding Source
Organization
Jmees Inc.
Organization
Division
Category of Funding Organization
Profit organization
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
National Cancer Center Ethics Committee
Address
1-1, Tsukiji 5-chome, Chuo-ku, Tokyo
Tel
03-3542-2511
irst@ncc.go.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2025 | Year | 06 | Month | 18 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
16
Results
AI assistance significantly improved sensitivity for ureter detection (43.5-58.1%, p < 0.001) and bladder detection (54.2-70.0%, p < 0.001) without reducing specificity. The additive effect of AI was greater in less experienced surgeons, with sensitivity improvements of 27.3% for ureter and 26.8% for bladder recognition. Ureter recognition was stable across surgical phases, while bladder recognition demonstrated moderate variability.
Results date posted
| 2025 | Year | 06 | Month | 18 | Day |
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Main results already published
Date of protocol fixation
| 2021 | Year | 11 | Month | 01 | Day |
Date of IRB
| 2022 | Year | 02 | Month | 13 | Day |
Anticipated trial start date
| 2022 | Year | 03 | Month | 01 | Day |
Last follow-up date
| 2026 | Year | 06 | Month | 30 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Materials and Methods: A deep learning model was trained on 13,934 ureter and 4,940 bladder images sourced from 41 institutions. Model performance was evaluated using the Dice coefficient. The performance of 16 surgeons from eight facilities was evaluated for identifying the ureter and bladder in pre-recorded surgical videos with and without AI support. Sensitivity and specificity were compared between AI-assisted and non-assisted conditions, stratified by surgeon experience.
Management information
Registered date
| 2025 | Year | 06 | Month | 18 | Day |
Last modified on
| 2025 | Year | 06 | Month | 18 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000066548
