OphthalmologyNCT04213183
Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images
An observational study of Ophthalmology and Artificial Intelligence, run by Sun Yat-sen University at 1 site in China.
Trial summary
Artificial Intelligence may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with hepatobiliary disorders. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Ophthalmology, Artificial Intelligence, Hepatobiliary Disease
- What is being tested?
- Hepatobiliary Disorders
- How many people
- 1,789 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2018to 31 Jan 2020
- How long will I take part?
- 1 Month
- 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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Slit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University, Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University, Affiliated Huadu Hospital of Southern Medical University, Nantian Medical Centre of Aikang Health Care, and Huanshidong Medical Centre of Aikang Health Care.
Who may be able to join
- 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.
Who cannot join
- Images with light leakage (>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis.
Trial locations and contacts
1 site in 1 country.
China1 site
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, 510000
About this study
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.
- Being tested
- Hepatobiliary Disorders
- Keywords
- Hepatobiliary Disease, Artificial Intelligence, Eye Images
- 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.
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 | Slit-lamp and retinal fundus images collected from Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University. Receives: Diagnostic Test: Hepatobiliary Disorders | |
| development dataset 02 | Slit-lamp and retinal fundus images collected from Affiliated Huadu Hospital of Southern Medical University. Receives: Diagnostic Test: Hepatobiliary Disorders | |
| development dataset 03 | Slit-lamp and retinal fundus images collected from Nantian Medical Centre of Aikang Health Care. Receives: Diagnostic Test: Hepatobiliary Disorders | |
| test dataset 01 | Slit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University. Receives: Diagnostic Test: Hepatobiliary Disorders | |
| test dataset 02 | Slit-lamp and retinal fundus images collected from Huanshidong Medical Centre of Aikang Health Care. Receives: Diagnostic Test: 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 (1)
sensitivity and specificity of the deep learning system
The investigators will calculate the sensitivity and 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 posted30 Dec 2019
- Study start1 Dec 2018
- Primary completion31 Jan 2020
- Study completion31 Jan 2020
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 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
- 1,789 participants (actual)
- Target duration
- 1 Month
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Third Affiliated Hospital, Sun Yat-Sen University, Affiliated Huadu Hospital of Southern Medical University, Aikang Health Care
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04213183
- Sponsor's protocol ID
- AEHD-2019
- Status last verified
- Aug 2020
- First submitted
- 25 Dec 2019
- Last updated
- 18 Aug 2020
- Results posted
- No
- Data monitoring committee
- Yes
- 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 18 Aug 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images
- ClinicalTrials.gov ID
- NCT04213183
- Acronym / sponsor ID
- AEHD-2019
- Keywords
- Hepatobiliary Disease, Artificial Intelligence, Eye Images
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,789 participants (actual)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Third Affiliated Hospital, Sun Yat-Sen University, Affiliated Huadu Hospital of Southern Medical University, Aikang Health Care
- Study start
- 1 Dec 2018 (actual)
- Primary completion
- 31 Jan 2020 (actual)
- Study completion
- 31 Jan 2020 (actual)
- First posted
- 30 Dec 2019 (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.
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.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 1 Month
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| development dataset 01 | – | Diagnostic Test: Hepatobiliary DisordersSlit-lamp and retinal fundus images collected from Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University. |
| development dataset 02 | – | Diagnostic Test: Hepatobiliary DisordersSlit-lamp and retinal fundus images collected from Affiliated Huadu Hospital of Southern Medical University. |
| development dataset 03 | – | Diagnostic Test: Hepatobiliary DisordersSlit-lamp and retinal fundus images collected from Nantian Medical Centre of Aikang Health Care. |
| test dataset 01 | – | Diagnostic Test: Hepatobiliary DisordersSlit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University. |
| test dataset 02 | – | Diagnostic Test: Hepatobiliary DisordersSlit-lamp and retinal fundus images collected from Huanshidong Medical Centre of Aikang Health Care. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Slit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University, Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University, Affiliated Huadu Hospital of Southern Medical University, Nantian Medical Centre of Aikang Health Care, and Huanshidong Medical Centre of Aikang Health Care.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (1 listed)
- Images with light leakage (>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis.
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 |
|---|---|---|
| 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, development dataset 03, 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
1 secondary outcome
sensitivity and 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 18 Aug 2020.
- 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, run by 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 3 inclusion and 1 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 18 Aug 2020. 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 18 Aug 2020; AzTrials last refreshed its copy on 28 Oct 2026.