UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000047465 |
|---|---|
| Receipt number | R000054085 |
| Scientific Title | Analysis of ocular pathological specimen images using image analysis software |
| Date of disclosure of the study information | 2022/05/01 |
| Last modified on | 2026/04/15 09:13:37 |
* This page includes information on clinical trials registered in UMIN clinical trial registed system.
* We don't aim to advertise certain products or treatments
Basic information
Public title
Analysis of ocular pathological specimen images using image analysis software
Acronym
Analysis of ocular pathological specimen images using image analysis software
Scientific Title
Analysis of ocular pathological specimen images using image analysis software
Scientific Title:Acronym
Analysis of ocular pathological specimen images using image analysis software
Region
| Japan |
Condition
Condition
Orbital disease
Classification by specialty
| Ophthalmology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
The purpose of this study is to develop a new quantitative evaluation method for the pathology of human eye tissue, which had been visually evaluated, to collect data by reading eye pathological sample images with software that can analyze images based on topological geometry and responding to the differentiation of existing normal structures, tumors, and inflammation changes.
Basic objectives2
Others
Basic objectives -Others
The purpose of this study is to develop a new quantitative evaluation method for the pathology of human eye tissue, which had been visually evaluated, to collect data by reading eye pathological sample images with software that can analyze images based on topological geometry and responding to the differentiation of existing normal structures, tumors, and inflammation changes.
Trial characteristics_1
Confirmatory
Trial characteristics_2
Explanatory
Developmental phase
Assessment
Primary outcomes
Quantitative evaluation results by topological geometric method. Specifically, the ratio (b1 / b0) of the 0-dimensional Vetch number, the one-dimensional Vetch number, and the Betch number obtained from each pathological sample image, and the threshold value of the bivaluation when it is obtained.
Basis for setting the main evaluation items: A method for calculating quantitative evaluation of the main outcome 1 is described. In this study, we analyze images using homology. In the analysis using homology, the vetch number of the image is an important concept. Hereinafter, the number of veches based on the definition limited to the two-dimensional image will be outlined.
All pathological images in this study are two-dimensional images.
Key secondary outcomes
1, How does the threshold of bivalanization change when the vetch number is obtained by normal structure, inflammation and neoplastic changes?
2,The correct diagnosis rate when the identification model which distinguishes the above structure, inflammation, tumor, emphysema change from the logistics regression model and the machine learning model for multiclass classification is created.
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
| 20 | years-old | < |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
1. From April 1, 2008 to December 31, 2021, a part of the eye was removed by surgery at Osaka City University Visual Pathology or Kobe Kaisei Hospital for the purpose of diagnosis and treatment of the disease, and patients who have received pathologic1. diagnosis who can obtain pathological sample images are eligible.
2.For comparison, pathological sample images of eye tissue in non-lesions are also subject to analysis.
3.Those who are 20 years of age or older at the time of diagnosis
Key exclusion criteria
1.Patients with complications that affect the evaluation
2. Patients who have offered not to participate in this study from the published information
Target sample size
100
Research contact person
Name of lead principal investigator
| 1st name | Mizuki |
| Middle name | |
| Last name | Tagami |
Organization
Osaka Metropolitan University
Division name
Ophthalmology and Visual sciences
Zip code
5458585
Address
1-4-3, Asahimachi Abeno-ku Osaka
TEL
0666453867
tagami.mizuki@med.osaka-cu.ac.jp
Public contact
Name of contact person
| 1st name | Mizuki |
| Middle name | |
| Last name | Tagami |
Organization
Osaka Metropolitan University
Division name
Ophthalmology and Visual sciences
Zip code
5458585
Address
1-4-3, Asahimachi Abeno-ku Osaka
TEL
+81666453867
Homepage URL
tagami.mizuki@med.osaka-cu.ac.jp
Sponsor or person
Institute
Osaka Metropolitan University
Institute
Department
Personal name
Funding Source
Organization
Osaka Metropolitan University
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
Osaka MetropolitanUniversity
Address
1-2-7 Asahimachi abeno-ku Osaka
Tel
06-6645-3456
gr-a-knky@omu.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
Other administrative information
Date of disclosure of the study information
| 2022 | Year | 05 | Month | 01 | Day |
Related information
URL releasing protocol
Publication of results
Partially published
Result
URL related to results and publications
https://pubmed.ncbi.nlm.nih.gov/37566457/
Number of participants that the trial has enrolled
100
Results
Purpose: The purpose of this study was to develop artificial intelligence algorithms that can distinguish between orbital and conjunctival mucosa-associated lymphoid tissue (MALT) lymphomas in pathological images.
Conclusion: Artificial intelligence algorithms can successfully distinguish HE images between orbital and conjunctival MALT lymphomas.
Results date posted
| 2026 | Year | 04 | Month | 15 | Day |
Results Delayed
| Delay expected |
Results Delay Reason
Because we are currently preparing to conduct further investigations.
Date of the first journal publication of results
Baseline Characteristics
To develop a diagnostic algorithm for ocular diseases based on patient background characteristics and pathological findings in cases for which pathological images were collected.
Participant flow
Adverse events
no
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Open public recruiting
Date of protocol fixation
| 2022 | Year | 04 | Month | 01 | Day |
Date of IRB
| 2022 | Year | 07 | Month | 28 | Day |
Anticipated trial start date
| 2022 | Year | 05 | Month | 01 | Day |
Last follow-up date
| 2027 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Observational research
Management information
Registered date
| 2022 | Year | 04 | Month | 12 | Day |
Last modified on
| 2026 | Year | 04 | Month | 15 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000054085
