UMIN-CTR Clinical Trial

Unique ID issued by UMIN UMIN000040321
Receipt number R000046008
Scientific Title Development of Diagnostic Artificial intelligence in the Ophthalmology
Date of disclosure of the study information 2020/05/07
Last modified on 2023/05/18 16:33:17

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Basic information

Public title

Development of Diagnostic Artificial intelligence in the Ophthalmology

Acronym

Development of Diagnostic Artificial intelligence in the Ophthalmology

Scientific Title

Development of Diagnostic Artificial intelligence in the Ophthalmology

Scientific Title:Acronym

Development of Diagnostic Artificial intelligence in the Ophthalmology

Region

Japan


Condition

Condition

Cataract, Dry Eye Disease, Keratoconus, Glaucoma, Allergic conjunctivitis, etc

Classification by specialty

Ophthalmology

Classification by malignancy

Others

Genomic information

NO


Objectives

Narrative objectives1

Trial of the Ophthalmological diagnostic AI

Basic objectives2

Efficacy

Basic objectives -Others


Trial characteristics_1


Trial characteristics_2


Developmental phase



Assessment

Primary outcomes

Clinical parameters by the Ophthalmological images

Key secondary outcomes



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

99 years-old >

Gender

Male and Female

Key inclusion criteria

Ophthalmological data in Keio University (IRB number:20090277,20170306,20180206) and Tsurumi University (IRB number:1634)

Key exclusion criteria

A case which does not want to commit to the clinical trial, no opt-in paper, other

Target sample size

300


Research contact person

Name of lead principal investigator

1st name Shimizu
Middle name
Last name Eisuke

Organization

Keio University School of Medicine

Division name

Department of Ophthalmology

Zip code

160-8582

Address

35 Shinanomachi, Shinjuku-ku

TEL

0353633972

Email

ophthalmolog1st.acek39@keio.jp


Public contact

Name of contact person

1st name Shimizu
Middle name
Last name Eisuke

Organization

Keio University School of Medicine

Division name

Department of Ophthalmology

Zip code

160-8582

Address

35 Shinanomachi, Shinjuku-ku

TEL

0353633972

Homepage URL


Email

ophthalmolog1st.acek39@keio.jp


Sponsor or person

Institute

Department of Ophthalmology, Keio University School of Medicine

Institute

Department

Personal name



Funding Source

Organization

Kakenhi

Organization

Division

Category of Funding Organization

Japanese Governmental office

Nationality of Funding Organization



Other related organizations

Co-sponsor


Name of secondary funder(s)



IRB Contact (For public release)

Organization

Keio University School of Medicine

Address

35 Shinanomachi, Shinjuku-ku

Tel

0333531211

Email

ophthalmolog1st.acek39@keio.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

2020 Year 05 Month 07 Day


Related information

URL releasing protocol

https://www.ctr.hosp.keio.ac.jp/news/003488.html

Publication of results

Partially published


Result

URL related to results and publications

https://www.ctr.hosp.keio.ac.jp/news/003488.html

Number of participants that the trial has enrolled

300

Results

The accuracy of tear film breakup time estimation was 0.789 (95% confidence interval (CI) 0.769 0.809), and the area under the receiver operating characteristic curve of this AI model was 0.877 (95% CI 0.861 0.893). The sensitivity and specificity of this AI model for the diagnosis of DED were 0.778 (95% CI 0.572 0.912) and 0.857 (95% CI 0.564 0.866), respectively. We successfully developed a novel AI-based diagnostic model for DED.

Results date posted

2023 Year 05 Month 18 Day

Results Delayed


Results Delay Reason


Date of the first journal publication of results


Baseline Characteristics

Japanese

Participant flow

Principal Investigator/Co-Principal Investigator selects the most appropriate case

Adverse events

none

Outcome measures

accuracy

Plan to share IPD


IPD sharing Plan description



Progress

Recruitment status

No longer recruiting

Date of protocol fixation

2020 Year 04 Month 01 Day

Date of IRB

2020 Year 05 Month 01 Day

Anticipated trial start date

2020 Year 05 Month 01 Day

Last follow-up date

2021 Year 03 Month 31 Day

Date of closure to data entry


Date trial data considered complete


Date analysis concluded



Other

Other related information

none


Management information

Registered date

2020 Year 05 Month 07 Day

Last modified on

2023 Year 05 Month 18 Day



Link to view the page

Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046008


Research Plan
Registered date File name

Research case data specifications
Registered date File name

Research case data
Registered date File name