UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000030427 |
|---|---|
| Receipt number | R000034700 |
| Scientific Title | Development of imaging diagnosis system for emergency patients by artificial intelligence |
| Date of disclosure of the study information | 2017/12/18 |
| Last modified on | 2017/12/18 15:33:20 |
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Basic information
Public title
Development of imaging diagnosis system for emergency patients by artificial intelligence
Acronym
Development of imaging diagnosis system
Scientific Title
Development of imaging diagnosis system for emergency patients by artificial intelligence
Scientific Title:Acronym
Development of imaging diagnosis system
Region
| Japan |
Condition
Condition
Emergency diseases and injuries
Classification by specialty
| Emergency medicine |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
Development of imaging diagnosis systems for emergency patients by artificial intelligence
Basic objectives2
Others
Basic objectives -Others
Development of systems and verification of accuracy
Trial characteristics_1
Exploratory
Trial characteristics_2
Pragmatic
Developmental phase
Not applicable
Assessment
Primary outcomes
Sensitivity and specificity of imaging diagnosis
Key secondary outcomes
Base
Study type
Others,meta-analysis etc
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
| Not applicable |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
All the patients whose CT data are preserved in the server
Key exclusion criteria
None
Target sample size
80000
Research contact person
Name of lead principal investigator
| 1st name | |
| Middle name | |
| Last name | Shigeki Kushimoto |
Organization
Tohoku University Graduate School of Medicine
Division name
Emergency and Critical Care Medicine
Zip code
Address
1-1 Seiryomachi, Aoba-ku, Sendai, 980-8574, Japan
TEL
022-717-7489
kussie@emergency-medicine.med.tohoku.ac.jp
Public contact
Name of contact person
| 1st name | |
| Middle name | |
| Last name | Daisuke Kudo |
Organization
Tohoku University Graduate School of Medicine
Division name
Emergency and Critical Care Medicine
Zip code
Address
1-1 Seiryomachi, Aoba-ku, Sendai, 980-8574, Japan
TEL
022-717-7489
Homepage URL
kudodaisuke@med.tohoku.ac.jp
Sponsor or person
Institute
Tohoku University
Institute
Department
Personal name
Funding Source
Organization
Self-funding by the profit organization which is included in the joint research team.
Organization
Division
Category of Funding Organization
Profit organization
Nationality of Funding Organization
Other related organizations
Co-sponsor
Hokkaido University Graduate School of Medicine
Diverta Inc.
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Address
Tel
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
東北大学病院（宮城）/Tohoku University Hospital
北海道大学病院（北海道）/Hokkaido University Hospital
Other administrative information
Date of disclosure of the study information
| 2017 | Year | 12 | 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
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
Preinitiation
Date of protocol fixation
| 2017 | Year | 12 | Month | 08 | Day |
Date of IRB
Anticipated trial start date
| 2018 | Year | 01 | Month | 01 | Day |
Last follow-up date
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Patients and Methods
Subjects are the patients admitted to the hospitals between October 2006 and September 2017 and whose CT data are preserved in the server. Imaging diagnosis includes all emergency diseases and injuries. We will collect the data including CT imaging, imaging diagnosis, and clinical information. We also collect the data of patients without abnormal CT findings as controls for deep learning. The area of CT image includes head, face, neck, chest, abdomen, and pelvis.
1st step
We will input the data to the machine learning software. The machine learning software will analyze and classify the data, then it will create algorithms for imaging diagnosis. We estimate that data from 70,000 patients will be needed to create the algorithms.
2nd step
We will use the data from different patients in this step. We will examine sensitivity and specificity of the algorithms that will be created in the 1st step by comparing with the imaging diagnosis previously reported by radiologists.
3rd step
We will repeat the 1st and 2nd steps in order to improve sensitivity and specificity of the algorithms for imaging diagnosis.
Management information
Registered date
| 2017 | Year | 12 | Month | 16 | Day |
Last modified on
| 2017 | Year | 12 | Month | 18 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000034700
| Research Plan | |
|---|---|
| Registered date | File name |
| Research case data specifications | |
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| Registered date | File name |
| Research case data | |
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| Registered date | File name |
