Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images
NCT ID: NCT04213183
Last Updated: 2020-08-18
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
1789 participants
OBSERVATIONAL
2018-12-01
2020-01-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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OTHER
PROSPECTIVE
Study Groups
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development dataset 01
Slit-lamp and retinal fundus images collected from Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University.
Hepatobiliary Disorders
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
development dataset 02
Slit-lamp and retinal fundus images collected from Affiliated Huadu Hospital of Southern Medical University.
Hepatobiliary Disorders
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
development dataset 03
Slit-lamp and retinal fundus images collected from Nantian Medical Centre of Aikang Health Care.
Hepatobiliary Disorders
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
test dataset 01
Slit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University.
Hepatobiliary Disorders
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
test dataset 02
Slit-lamp and retinal fundus images collected from Huanshidong Medical Centre of Aikang Health Care.
Hepatobiliary Disorders
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
Interventions
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Hepatobiliary Disorders
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
Eligibility Criteria
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Inclusion Criteria
* More than 90% of the fundus image area including four main regions (optic disk, macular, upper and lower retinal vessel archs) are easy to read and discriminate.
* More than 90% of the slit-lamp image area including three main regions (sclera, pupil, and lens) are easy to read and discriminate.
Exclusion Criteria
ALL
Yes
Sponsors
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Third Affiliated Hospital, Sun Yat-Sen University
OTHER
Affiliated Huadu Hospital of Southern Medical University
UNKNOWN
Aikang Health Care
UNKNOWN
Sun Yat-sen University
OTHER
Responsible Party
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Haotian Lin
Principal Investigator
Locations
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Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, China
Countries
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Other Identifiers
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AEHD-2019
Identifier Type: -
Identifier Source: org_study_id
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