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Name:
UMIN ID:

Recruitment status Preinitiation
Unique ID issued by UMIN UMIN000030427
Receipt No. 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

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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
Email 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
Email kudodaisuke@med.tohoku.ac.jp

Sponsor
Institute Tohoku University
Institute
Department

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
Email

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
URL(English) https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000034700

Research Plan
Registered date File name

Research case data specifications
Registered date File name

Research case data
Registered date File name


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