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Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images | Clinical Trials | Clareo Health

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Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

NCT04213183•Sun Yat-sen University

View on ClinicalTrials.gov

Summary

Artificial Intelligence may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with hepatobiliary disorders. We conducted a pioneer work to explore the association between the eye and liver via deep learning, to develop and evaluate different deep learning models to predict the hepatobiliary disease by using ocular images.

Eligibility

Age

All ages

Sex

ALL

Healthy Volunteers

Yes

Inclusion Criteria

•The quality of fundus and slit-lamp images should clinical acceptable.
•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

•Images with light leakage (\>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis.

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Guangzhou, Guangdong, China

Key Dates

Start Date

December 1, 2018

Primary Completion Date

January 31, 2020

Completion Date

January 31, 2020

Last Updated

August 18, 2020

Enrollment

1,789

ACTUAL participants

Conditions

OphthalmologyArtificial IntelligenceHepatobiliary Disease

Interventions

Hepatobiliary Disorders

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Data Source & Attribution

This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.

ClinicalTrials.gov last update: August 18, 2020Data synced to Clareo: March 29, 2026

Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.

Neither the United States Government nor Clareo Health make any warranties regarding the data. Check ClinicalTrials.gov frequently for updates.

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Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Inclusion Criteria

Exclusion Criteria

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