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.

Study Overview

• Status: Completed
• Conditions: Systemic Diseases; Ophthalmology; Hepatobiliary Diseases; Artificial Intelligence (AI)
• Intervention / Treatment: Diagnostic test: Systemic diseases such as Hepatobiliary Disorders

• Study Type: Observational
• Enrollment (Actual): 775

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510000
      • Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University and Pazhou Medical Centre of Aikang Health Care

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
development dataset 01; ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University	Diagnostic test: Systemic diseases such as Hepatobiliary Disorders; The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
development dataset 02; ocular images collected from Pazhou Medical Centre of Aikang Health Care	Diagnostic test: Systemic diseases such as Hepatobiliary Disorders; The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
test dataset 01; ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University	Diagnostic test: Systemic diseases such as Hepatobiliary Disorders; The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
test dataset 02; ocular images collected from Pazhou Medical Centre of Aikang Health Care	Diagnostic test: Systemic diseases such as Hepatobiliary Disorders; The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	baseline

Secondary Outcome Measures

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

Collaborators and Investigators

• Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
• Collaborators: Third Affiliated Hospital, Sun Yat-Sen University; Aikang Health Care

Study record dates

Study Major Dates

• Study Start (Actual): April 10, 2020
• Primary Completion (Actual): July 30, 2024
• Study Completion (Actual): July 30, 2024

Study Registration Dates

• First Submitted: May 6, 2026
• First Submitted That Met QC Criteria: May 6, 2026
• First Posted (Actual): May 12, 2026

Study Record Updates

• Last Update Posted (Actual): May 14, 2026
• Last Update Submitted That Met QC Criteria: May 11, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases
• Other Study ID Numbers: 2019KYPJ163

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No