OphthalmologyNCT07581925
Detection of Systemic Diseases Such as Hepatobiliary Diseases From Ocular Images Via Deep Learning
An observational study of Ophthalmology and Artificial Intelligence (AI), run by Zhongshan Ophthalmic Center, Sun Yat-sen University at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Ophthalmology, Artificial Intelligence (AI), Hepatobiliary Diseases
- What is being tested?
- Systemic diseases such as Hepatobiliary Disorders
- How many people
- 730 participants
- Where
- 1 siteChina
- Study dates
- 10 Apr 2020to 30 Jul 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University and Pazhou Medical Centre of Aikang Health Care
Who may be able to join
- The quality of ocular images should clinical acceptable.
- Complete clinical information such as baseline demographic characteristics, the history of systematic diseases and so on.
Who cannot join
- Individuals diagnosed with severe eye diseases or acute systematic diseases.
- Incompatible with ocular examinations.
Trial locations and contacts
1 site in 1 country.
China1 site
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, 510000
About this study
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.
- Being tested
- Systemic diseases such as Hepatobiliary Disorders
- Medical subject headings
- Digestive System Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Systemic diseases such as Hepatobiliary DisordersDiagnostic test
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
| Study group | Role | What they receive |
|---|---|---|
| development dataset 01 | ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University Receives: Diagnostic Test: Systemic diseases such as Hepatobiliary Disorders | |
| development dataset 02 | ocular images collected from Pazhou Medical Centre of Aikang Health Care Receives: Diagnostic Test: Systemic diseases such as Hepatobiliary Disorders | |
| test dataset 01 | ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University Receives: Diagnostic Test: Systemic diseases such as Hepatobiliary Disorders | |
| test dataset 02 | ocular images collected from Pazhou Medical Centre of Aikang Health Care Receives: Diagnostic Test: Systemic diseases such as Hepatobiliary Disorders |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: baseline
Secondary outcomes (2)
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
Measured: 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
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 May 2026
- Study start10 Apr 2020
- Primary completion30 Jul 2024
- Study completion30 Jul 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for OphthalmologyStudies of the same condition, open and under way.
- More trials run by Zhongshan Ophthalmic Center, Sun Yat-sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 730 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Third Affiliated Hospital, Sun Yat-Sen University, Aikang Health Care
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07581925
- Sponsor's protocol ID
- 2019KYPJ163
- Status last verified
- May 2026
- First submitted
- 6 May 2026
- Last updated
- 1 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection of Systemic Diseases Such as Hepatobiliary Diseases From Ocular Images Via Deep Learning
- ClinicalTrials.gov ID
- NCT07581925
- Acronym / sponsor ID
- 2019KYPJ163
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 730 participants (actual)
- Lead sponsor
- Zhongshan Ophthalmic Center, Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Third Affiliated Hospital, Sun Yat-Sen University, Aikang Health Care
- Study start
- 10 Apr 2020 (actual)
- Primary completion
- 30 Jul 2024 (actual)
- Study completion
- 30 Jul 2024 (actual)
- First posted
- 12 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| development dataset 01 | – | Diagnostic Test: Systemic diseases such as Hepatobiliary Disordersocular images collected from the Third Affiliated Hospital of Sun Yat-sen University |
| development dataset 02 | – | Diagnostic Test: Systemic diseases such as Hepatobiliary Disordersocular images collected from Pazhou Medical Centre of Aikang Health Care |
| test dataset 01 | – | Diagnostic Test: Systemic diseases such as Hepatobiliary Disordersocular images collected from the Third Affiliated Hospital of Sun Yat-sen University |
| test dataset 02 | – | Diagnostic Test: Systemic diseases such as Hepatobiliary Disordersocular images collected from Pazhou Medical Centre of Aikang Health Care |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- ocular images collected from the Third Affiliated Hospital of Sun Yat-sen University and Pazhou Medical Centre of Aikang Health Care
Key inclusion criteria (2 listed)
- The quality of ocular images should clinical acceptable.
- Complete clinical information such as baseline demographic characteristics, the history of systematic diseases and so on.
Key exclusion criteria (2 listed)
- Individuals diagnosed with severe eye diseases or acute systematic diseases.
- Incompatible with ocular examinations.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Systemic diseases such as Hepatobiliary DisordersThe training dataset was used to train the deep learning model, which was validated and tested by the other two datasets. | Diagnostic test | development dataset 01, development dataset 02, test dataset 01, test dataset 02 |
Primary outcome
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
2 secondary outcomes
sensitivity of the deep learning system
Time frame: baseline
specificity of the deep learning system
Time frame: baseline
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Sep 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ophthalmology and Artificial Intelligence (AI), run by Zhongshan Ophthalmic Center, Sun Yat-sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
area under the receiver operating characteristic curve of the deep learning system (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Sep 2026.