UMIN-ICDS Clinical Trial
| Unique ID issued by UMIN | UMIN000062517 |
|---|---|
| Receipt number | R000071549 |
| Scientific Title | The study of AI based Vascular healing in disease monitoring of UC: a multi-center prospective cohort study |
| Date of disclosure of the study information | 2026/08/07 |
| Last modified on | 2026/08/07 17:59:15 |
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Basic information
Public title
The study of AI based Vascular healing in disease monitoring of UC: a multi-center prospective cohort study
Acronym
AI-VISION Trial
Scientific Title
The study of AI based Vascular healing in disease monitoring of UC: a multi-center prospective cohort study
Scientific Title:Acronym
AI-VISION Trial
Region
| Japan |
Condition
Condition
Ulcerative colitis
Classification by specialty
| Gastroenterology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
To evaluate the predictive performance and risk stratification ability of an artificial intelligence based narrow band imaging system for relapse within 12 months as a clinically important outcome
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Exploratory
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Time from colonoscopy to clinical relapse within 12 months
Clinical relapse is defined using the patient reported outcome measure PRO 2 as a rectal bleeding score of 1 or higher and a stool frequency score of 2 or higher
The predictive performance and risk stratification ability of vascular healing assessment by the artificial intelligence narrow band imaging system and histological assessment using the Geboes grade for clinical relapse within 12 months will be evaluated using the C statistic for time to event data
Key secondary outcomes
The 12 month relapse rate will be estimated using the Kaplan Meier method and stratified by vascular healing assessment using the artificial intelligence narrow band imaging system Geboes score and Nancy Histological Index Additional subgroup analyses will be performed according to clinical characteristics including treatment disease extent and duration of remission
An artificial intelligence based quantitative score will be calculated using white light endoscopic images and its association with relapse within 12 months will be evaluated The correlation between this score and established endoscopic scores including the Mayo Endoscopic Subscore and Ulcerative Colitis Endoscopic Index of Severity will also be evaluated
An artificial intelligence based digital pathology system will be applied to histopathological specimens to quantify neutrophil infiltration and colonic mucin content The associations between these quantitative measures and relapse within 12 months will be evaluated
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
1. Age 18 years or older
2. Confirmed diagnosis of ulcerative colitis
3. Steroid-free clinical remission, defined as a partial Mayo score of 1 or less, maintained for at least 6 months
4. Scheduled to undergo colonoscopy based on clinical necessity
Key exclusion criteria
1. Unable to provide informed consent
2. Contraindication to colonoscopy or biopsy
3. Inadequate bowel preparation
4. Serious comorbidities
5. Pregnancy or breastfeeding
6. Change or escalation of treatment within the previous 3 months
7. Ulcerative proctitis
8. Considered unsuitable for participation by the investigator
Target sample size
300
Research contact person
Name of lead principal investigator
| 1st name | Kazuo |
| Middle name | |
| Last name | Ohtsuka |
Organization
Showa Medical University Northern Yokohama Hospital
Division name
Digestive Disease Center
Zip code
224-8503
Address
35-1 Chigasaki-chuo, Tsuzuki, Yokohama, Kanagawa, Japan
TEL
045-949-7000
ohts@med.showa-u.ac.jp
Public contact
Name of contact person
| 1st name | Yasuharu |
| Middle name | |
| Last name | Maeda |
Organization
Showa Medical University Northern Yokohama Hospital
Division name
Digestive Disease Center
Zip code
224-8503
Address
35-1 Chigasaki-chuo, Tsuzuki, Yokohama, Kanagawa, Japan
TEL
045-949-5840
Homepage URL
yasuharu@med.showa-u.ac.jp
Sponsor or person
Institute
Showa Medical University
Institute
Department
Personal name
Funding Source
Organization
Self funding
Organization
Division
Category of Funding Organization
Self funding
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Ethics Committee for Research Involving Human Subjects at Showa Medical University
Address
1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8555, Japan
Tel
03-3784-8129
m-rinri@ofc.showa-u.ac.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
昭和医科大学横浜市北部病院(神奈川県)、横浜市立市民病院(神奈川県)、東京科学大学病院(東京都)、旭川医科大学(北海道)、北里大学北里研究所病院(東京都)、大船中央病院(神奈川県)、昭和医科大学江東豊洲病院(東京都)
Other administrative information
Date of disclosure of the study information
| 2026 | Year | 08 | Month | 07 | 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
Results
Results date posted
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
Open public recruiting
Date of protocol fixation
| 2026 | Year | 05 | Month | 07 | Day |
Date of IRB
| 2026 | Year | 07 | Month | 31 | Day |
Anticipated trial start date
| 2026 | Year | 08 | Month | 07 | Day |
Last follow-up date
| 2029 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
This is a multicenter prospective observational cohort study. At baseline colonoscopy performed as part of routine clinical care, white-light and NBI images, histological specimens, patient characteristics, treatment information, and laboratory data are collected. After colonoscopy, vascular healing is assessed using the AI-NBI system, an AI-based quantitative score is calculated from white-light images, and histological activity is assessed using the Geboes score and Nancy Histological Index. Participants are followed for 12 months from baseline colonoscopy. PRO-2, treatment changes, hospitalization, surgery, and clinical relapse are assessed during routine clinical visits. The predictive performance of AI-NBI vascular healing assessment and histological assessment for clinical relapse within 12 months is compared.
Management information
Registered date
| 2026 | Year | 08 | Month | 07 | Day |
Last modified on
| 2026 | Year | 08 | Month | 07 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000071549
