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OpenTrials
Completed

NCT Number: NCT04213183

Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

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.

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Key information

Conditions

Ophthalmology Artificial Intelligence Digestive System Diseases Hepatobiliary Disease

Sex eligibility

All sexes

Study type

Observational

Primary location

Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Guangzhou, Guangdong, 510000, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Hepatobiliary Disorders

Diagnostic Test

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

Primary outcomes

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

    Time frame: 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 outcomes

  1. sensitivity and specificity of the deep learning system

    Time frame: baseline

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

Sponsors and collaborators

Lead sponsor

Sun Yat-sen University

Other

Collaborators

  • Affiliated Huadu Hospital of Southern Medical University
  • Aikang Health Care
  • Third Affiliated Hospital, Sun Yat-Sen University

Registry information

Important dates

Study start
2018
Primary completion
2020
Study completion
2020
First posted
Dec 30, 2019
Registry last updated
Aug 18, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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