Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Sun Yat-sen University1,789 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

1,789

Conditions

Ophthalmology Artificial Intelligence Hepatobiliary Disease

Interventions

Hepatobiliary DisordersDIAGNOSTIC_TEST

The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.

Eligibility

Sex: ALLHealthy volunteers:

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

area under the receiver operating characteristic curve of the deep learning system

The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors

Time frame: baseline

Secondary Outcomes

sensitivity and specificity of the deep learning system

The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors

Time frame: baseline

Data from ClinicalTrials.gov

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