Artificial IntelligenceAGENCT03268031
Artificial Intelligence-assissted Glaucoma Evaluation
An observational study of Artificial Intelligence and Glaucoma, run by Sun Yat-sen University at 1 site in China.
Trial summary
Glaucoma is currently the second leading cause of irreversible blindness in the world. Our study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Glaucoma
- What is being tested?
- Visual field and OCT tests
- How many people
- 10,800 participants
- Where
- 1 siteChina
- Study dates
- 15 Aug 2017to 1 Feb 2020
- 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: Anyone who can complete visual field test and have BCVA>0.1 can be enrolled. We will collect visual field test result and OCT images of both glaucoma and non-glaucoma patients.
Who may be able to join
- BCVA>0.1
- able to complete reliable visual field test
- no history of intraocular surgery or fundus laser
Who cannot join
- 1. unable to complete visual field test
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development of Artificial Intelligence-assissted Diagnostic Program of Glaucoma
Glaucoma is currently the second leading cause of irreversible blindness in the world. Our study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.
Read the detailed description
Glaucoma is currently the second leading cause of irreversible blindness in the world, which brings heavy burden to human society. Compared to other ocular diseases, diagnostic process of glaucoma is complicated depends on multiple test results, including visual field test, OCT, etc. How to diagnose glaucoma correctly and fast has always been a hot topic in glaucoma researches. Artificial intelligence is used to study and develop theories and methods that can help simulate and extend human intelligence, which has been utilized in a lot of research fields such as automatic drive and medicine. The study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.
- Conditions
- Artificial Intelligence, Glaucoma
- Being tested
- Visual field and OCT tests
- Keywords
- artificial intelligence, glaucoma
- Medical subject headings
- Glaucoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Visual field and OCT testsDiagnostic test
Visual field test and OCT are commonly used essential tests to make accurate diagnosis of glaucoma. Algorithms to classify Visual field and OCT tests would both be developed and verified.
| Study group | Role | What they receive |
|---|---|---|
| Glaucoma patients | Glaucoma patients will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning. Receives: Diagnostic Test: Visual field and OCT tests | |
| Non-glaucoma participants | Non-glaucoma participants will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning. Receives: Diagnostic Test: Visual field and OCT tests |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of diagnosis by artificial intelligence algorithm
Accuracy of diagnosis by artificial intelligence algorithm and compare this result with glaucoma specialists
Measured: from August 2017 to February 2021
Secondary outcomes (2)
Sensitivity of diagnosis by artificial intelligence algorithm
Sensitivity of diagnosis by artificial intelligence algorithm
Measured: from August 2017 to February 2021
Specificity of diagnosis by artificial intelligence algorithm
Specificity of diagnosis by artificial intelligence algorithm
Measured: from August 2017 to February 2021
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Aug 2017
- Study start15 Aug 2017
- Primary completion1 Dec 2019
- Study completion1 Feb 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 10,800 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Chinese Academy of Sciences
- Responsible party
- Principal investigator, Xiulan Zhang, Sun Yat-sen University
- Investigators
- Xiulan Zhang, Doctor, Principal investigator, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03268031
- Sponsor's protocol ID
- ProjectAGE
- Acronym
- AGE
- Status last verified
- Oct 2020
- First submitted
- 29 Aug 2017
- Last updated
- 22 Oct 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Diprose W, Buist N. Artificial intelligence in medicine: humans need not apply? N Z Med J. 2016 May 6;129(1434):73-6.
PubMed 27349266Quigley HA. Glaucoma. Lancet. 2011 Apr 16;377(9774):1367-77. doi: 10.1016/S0140-6736(10)61423-7. Epub 2011 Mar 30.
PubMed 21453963Asaoka R, Murata H, Iwase A, Araie M. Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier. Ophthalmology. 2016 Sep;123(9):1974-80. doi: 10.1016/j.ophtha.2016.05.029. Epub 2016 Jul 7.
PubMed 27395766
Study at a glance
From the registry record, last updated by the sponsor on 22 Oct 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of Artificial Intelligence-assissted Diagnostic Program of Glaucoma
- ClinicalTrials.gov ID
- NCT03268031
- Acronym / sponsor ID
- AGE / ProjectAGE
- Conditions
- Artificial Intelligence, Glaucoma
- Keywords
- artificial intelligence, glaucoma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 10,800 participants (actual)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Chinese Academy of Sciences
- Study start
- 15 Aug 2017 (actual)
- Primary completion
- 1 Dec 2019 (actual)
- Study completion
- 1 Feb 2020 (actual)
- First posted
- 31 Aug 2017 (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.
Glaucoma is currently the second leading cause of irreversible blindness in the world. Our study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.
Detailed description
Glaucoma is currently the second leading cause of irreversible blindness in the world, which brings heavy burden to human society. Compared to other ocular diseases, diagnostic process of glaucoma is complicated depends on multiple test results, including visual field test, OCT, etc. How to diagnose glaucoma correctly and fast has always been a hot topic in glaucoma researches. Artificial intelligence is used to study and develop theories and methods that can help simulate and extend human intelligence, which has been utilized in a lot of research fields such as automatic drive and medicine. The study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Glaucoma patients | – | Diagnostic Test: Visual field and OCT testsGlaucoma patients will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning. |
| Non-glaucoma participants | – | Diagnostic Test: Visual field and OCT testsNon-glaucoma participants will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Anyone who can complete visual field test and have BCVA>0.1 can be enrolled. We will collect visual field test result and OCT images of both glaucoma and non-glaucoma patients.
Key inclusion criteria (3 listed)
- BCVA>0.1
- able to complete reliable visual field test
- no history of intraocular surgery or fundus laser
Key exclusion criteria (1 listed)
- 1. unable to complete visual field test
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 |
|---|---|---|
| Visual field and OCT testsVisual field test and OCT are commonly used essential tests to make accurate diagnosis of glaucoma. Algorithms to classify Visual field and OCT tests would both be developed and verified. | Diagnostic test | Glaucoma patients, Non-glaucoma participants |
Primary outcome
Accuracy of diagnosis by artificial intelligence algorithm
Time frame: from August 2017 to February 2021
Accuracy of diagnosis by artificial intelligence algorithm and compare this result with glaucoma specialists
2 secondary outcomes
Sensitivity of diagnosis by artificial intelligence algorithm
Time frame: from August 2017 to February 2021
Specificity of diagnosis by artificial intelligence algorithm
Time frame: from August 2017 to February 2021
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 22 Oct 2020.
- Results publication: PubMed 27349266Diprose W, Buist N. Artificial intelligence in medicine: humans need not apply? N Z Med J. 2016 May 6;129(1434):73-6.
- Results publication: PubMed 21453963Quigley HA. Glaucoma. Lancet. 2011 Apr 16;377(9774):1367-77. doi: 10.1016/S0140-6736(10)61423-7. Epub 2011 Mar 30.
- Results publication: PubMed 27395766Asaoka R, Murata H, Iwase A, Araie M. Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier. Ophthalmology. 2016 Sep;123(9):1974-80. doi: 10.1016/j.ophtha.2016.05.029. Epub 2016 Jul 7.
- 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.
Xiulan Zhang, Doctor principal investigator
Sun Yat-sen University
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 Artificial Intelligence and Glaucoma, 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 22 Oct 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?
Accuracy of diagnosis by artificial intelligence algorithm (time frame: from August 2017 to February 2021). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03268031, which also lists 3 linked publications.
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 22 Oct 2020.