Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04213183

Enrollment

1789

Registered

2019-12-30

Start date

2018-12-01

Completion date

2020-01-31

Last updated

2020-08-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence, Hepatobiliary Disease, Ophthalmology

Keywords

Hepatobiliary Disease, Artificial Intelligence, Eye Images

Brief 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.

Interventions

DIAGNOSTIC_TESTHepatobiliary Disorders

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

Study design

Observational model

OTHER

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

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.

Design outcomes

Primary

Measure	Time frame	Description
area under the receiver operating characteristic curve of the deep learning system	baseline	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

Secondary

Measure	Time frame	Description
sensitivity and specificity of the deep learning system	baseline	The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026