Detection of Systemic Diseases Such as Hepatobiliary Diseases From Ocular Images Via Deep Learning
1 other identifier
observational
775
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Apr 2020
Longer than P75 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
April 10, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 30, 2024
CompletedStudy Completion
Last participant's last visit for all outcomes
July 30, 2024
CompletedFirst Submitted
Initial submission to the registry
May 6, 2026
CompletedFirst Posted
Study publicly available on registry
May 12, 2026
CompletedMay 14, 2026
May 1, 2026
4.3 years
May 6, 2026
May 11, 2026
Conditions
Outcome Measures
Primary Outcomes (1)
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 Outcomes (2)
sensitivity of the deep learning system
baseline
specificity of the deep learning system
baseline
Study Arms (4)
development dataset 01
ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University
development dataset 02
ocular images collected from Pazhou Medical Centre of Aikang Health Care
test dataset 01
ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University
test dataset 02
ocular images collected from Pazhou Medical Centre of Aikang Health Care
Interventions
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
Eligibility Criteria
ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University and Pazhou Medical Centre of Aikang Health Care
You may qualify if:
- The quality of ocular images should clinical acceptable.
- Complete clinical information such as baseline demographic characteristics, the history of systematic diseases and so on.
You may not qualify if:
- Individuals diagnosed with severe eye diseases or acute systematic diseases.
- Incompatible with ocular examinations.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, 510000, China
MeSH Terms
Conditions
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
May 6, 2026
First Posted
May 12, 2026
Study Start
April 10, 2020
Primary Completion
July 30, 2024
Study Completion
July 30, 2024
Last Updated
May 14, 2026
Record last verified: 2026-05
Data Sharing
- IPD Sharing
- Will not share