Angle Closure GlaucomaNCT04242108
Diagnostic Performance of Deep Learning for Angle Closure
An observational study of Angle Closure Glaucoma, run by Sun Yat-sen University at 1 site in China.
Trial summary
Primary angle closure diseases (PACD) are commonly seen in Asia. In clinical practice, gonioscopy is the gold standard for angle width classification in PACD patietns. However, gonioscopy is a contact examination and needs a long learning curve. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Angle Closure Glaucoma
- What is being tested?
- Deep learning algorithm based on AS-OCT scans
- How many people
- 3,000 participants
- Where
- 1 siteChina
- Study dates
- 15 Jan 2019to Mar 2022
- 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: The training and primary validation datasets were collected from the databases of electronic medical and research records at Zhongshan Ophthalmic Center from September 1, 2016, to September 1, 2019. The external test dataset was obtained from the Singapore Eye Research Institute (SERI), Singapore during June 2008 to November 2019, and the Chulalongkorn University and King Chulalongkorn Memorial Hospital (KCMH, Bangkok, Thailand) from October, 2019 to April, 2020.
The inclusion criteria in the study were as follows: (1) All participants must be ≥ 18 years old; (2) Study subjects had a previous diagnosis of the ACA status (narrow or open, PAS or non-PAS) based on gonioscopy, SS-OCT scans and medical history records. Exclusion criteria of the data include: (1) poor compliance in receiving gonioscopy examination; (2) unclear AS-OCT scans due to blinking or out of focus; (3) recent use of miotics within a month; 4) secondary angle closure sue to subluxation or dislocation, uveitis, neovascular glaucoma, et al.; 5) history of ocular surgery or laser iridotomy; 6) patients who previously had an episode of primary angle closure (which was obtained on history by asking the patients).
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Diagnostic Performance of Deep Convolutional Neural Networks for Angle Closure Glaucoma: an International Multicenter Study
Primary angle closure diseases (PACD) are commonly seen in Asia. In clinical practice, gonioscopy is the gold standard for angle width classification in PACD patietns. However, gonioscopy is a contact examination and needs a long learning curve. Anterior segment optical coherence tomography (AS-OCT) is a non-contact test which can obtain three dimensional images of the anterior segment within seconds. Therefore, the investigators designed the study to verify if AS-OCT based deep learning algorithm is able to detect the PACD subjects diagnosed by gonioscopy.
- Conditions
- Angle Closure Glaucoma
- Being tested
- Deep learning algorithm based on AS-OCT scans
- Medical subject headings
- Glaucoma, Angle-Closure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning algorithm based on AS-OCT scansDiagnostic test
The OCT scans of study subjects would be imported into the algorithm. Automated classfication of angle width and detection of synechia would be performed by the algorithm. The diagnostic performance of the algorithm would be compared with gonioscopy records.
| Study group | Role | What they receive |
|---|---|---|
| Angle closure group | Not described Receives: Diagnostic Test: Deep learning algorithm based on AS-OCT scans | |
| Open angle group | Not described Receives: Diagnostic Test: Deep learning algorithm based on AS-OCT scans | |
| Peripheral synechia (PAS) group | Not described Receives: Diagnostic Test: Deep learning algorithm based on AS-OCT scans | |
| Non-peripheral synechia (PAS) group | Not described Receives: Diagnostic Test: Deep learning algorithm based on AS-OCT scans |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under receiver operating curve (AUC)
AUC value of the deep learning algorithm in angle width classfication and synechia detection
Measured: Immediately after obtaining the AS-OCT images
Secondary outcomes (1)
Sensitivity and specificity
Sensitivity and specificity of the automated algorithm in angle width classfication and synechia detection
Measured: Immediately after obtaining the AS-OCT images
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jan 2020
- Study start15 Jan 2019
- Primary completionDec 2021 (estimated)
- Study completionMar 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Angle Closure GlaucomaStudies 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
- Retrospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiulan Zhang, Zhongshan Ophthalmic Center, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04242108
- Sponsor's protocol ID
- 2018KYPJ074
- Status last verified
- Apr 2021
- First submitted
- 23 Jan 2020
- Last updated
- 8 Apr 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The imaging data of study subjects would be available to other researchers upon reasonable request. Part of the data would be open as public datasets after the related article is published.
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Performance of Deep Convolutional Neural Networks for Angle Closure Glaucoma: an International Multicenter Study
- ClinicalTrials.gov ID
- NCT04242108
- Acronym / sponsor ID
- 2018KYPJ074
- Conditions
- Angle Closure Glaucoma
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 15 Jan 2019 (actual)
- Primary completion
- Dec 2021 (estimated)
- Study completion
- Mar 2022 (estimated)
- First posted
- 27 Jan 2020 (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.
Primary angle closure diseases (PACD) are commonly seen in Asia. In clinical practice, gonioscopy is the gold standard for angle width classification in PACD patietns. However, gonioscopy is a contact examination and needs a long learning curve. Anterior segment optical coherence tomography (AS-OCT) is a non-contact test which can obtain three dimensional images of the anterior segment within seconds. Therefore, the investigators designed the study to verify if AS-OCT based deep learning algorithm is able to detect the PACD subjects diagnosed by gonioscopy.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Angle closure group | – | Diagnostic Test: Deep learning algorithm based on AS-OCT scans |
| Open angle group | – | Diagnostic Test: Deep learning algorithm based on AS-OCT scans |
| Peripheral synechia (PAS) group | – | Diagnostic Test: Deep learning algorithm based on AS-OCT scans |
| Non-peripheral synechia (PAS) group | – | Diagnostic Test: Deep learning algorithm based on AS-OCT scans |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The training and primary validation datasets were collected from the databases of electronic medical and research records at Zhongshan Ophthalmic Center from September 1, 2016, to September 1, 2019. The external test dataset was obtained from the Singapore Eye Research Institute (SERI), Singapore during June 2008 to November 2019, and the Chulalongkorn University and King Chulalongkorn Memorial Hospital (KCMH, Bangkok, Thailand) from October, 2019 to April, 2020.
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 |
|---|---|---|
| Deep learning algorithm based on AS-OCT scansThe OCT scans of study subjects would be imported into the algorithm. Automated classfication of angle width and detection of synechia would be performed by the algorithm. The diagnostic performance of the algorithm would be compared with gonioscopy records. | Diagnostic test | Angle closure group, Non-peripheral synechia (PAS) group, Open angle group, Peripheral synechia (PAS) group |
Primary outcome
Area under receiver operating curve (AUC)
Time frame: Immediately after obtaining the AS-OCT images
AUC value of the deep learning algorithm in angle width classfication and synechia detection
1 secondary outcome
Sensitivity and specificity
Time frame: Immediately after obtaining the AS-OCT images
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Apr 2021.
- Individual participant dataSharing planned: Yes. The imaging data of study subjects would be available to other researchers upon reasonable request. Part of the data would be open as public datasets after the related article is published.
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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 Angle Closure 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?
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: status not confirmed, as of the sponsor's last update on 8 Apr 2021. 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?
Results are not available in the source record.
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 receiver operating curve (AUC) (time frame: Immediately after obtaining the AS-OCT images). 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 8 Apr 2021.