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OpenTrials
Completed

NCT Number: NCT07581925

Detection of Systemic Diseases Such as Hepatobiliary Diseases From Ocular Images Via Deep Learning

Oculomics is an emerging interdisciplinary field that deciphers multi-dimensional, high-throughput ocular data to predict, diagnose, and monitor systemic diseases and health span.In recent years, artificial Intelligence may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with systemic diseases. The investigators conducted a survey to explore the association between the eye and systemic diseases via deep learning, to develop and evaluate different deep learning models to predict the systemic diseases such as hepatobiliary disease by using ocular images.

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Key information

Conditions

Ophthalmology Artificial Intelligence (AI) Digestive System Diseases Hepatobiliary Diseases Systemic Diseases

Age range

18 year and older

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 ocular images should clinical acceptable.
  • Complete clinical information such as baseline demographic characteristics, the history of systematic diseases and so on.

Exclusion criteria

  • Individuals diagnosed with severe eye diseases or acute systematic diseases.
  • Incompatible with ocular examinations.

Treatment and study plan

Systemic diseases such as 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 of the deep learning system

    Time frame: baseline

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

  2. specificity of the deep learning system

    Time frame: baseline

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

Sponsors and collaborators

Lead sponsor

Zhongshan Ophthalmic Center, Sun Yat-sen University

Other

Collaborators

  • Aikang Health Care
  • Third Affiliated Hospital, Sun Yat-Sen University

Registry information

Important dates

Study start
2020
Primary completion
2024
Study completion
2024
First posted
May 12, 2026
Registry last updated
May 14, 2026

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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