GlaucomaNCT06012058
Glaucoma Screening With Artificial Intelligence
A study testing ROTA assessment by AI and Optic disc assessment by AI in people with Glaucoma, run by The University of Hong Kong at 2 sites in Hong Kong.
Trial summary
This randomized clinical trial aims to compare the diagnostic performance of two AI-enabled screening strategies - ROTA (RNFL optical texture analysis) assessment versus optic disc photography - in detecting glaucoma within a population-based sample. Read more about this study
- Who is it for?
- Ages 50 years and olderHealthy volunteers can take part
- Conditions the study is for
- Glaucoma
- What is being tested?
- ROTA assessment by AI and Optic disc assessment by AI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 3,175 participants
- Where
- 2 sitesHong Kong
- Study dates
- 26 Aug 2023to 25 Feb 2025
- Phase
- Interventional
- 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 50 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Individuals aged 50 years or above
Who cannot join
- Physically incapacitated
- Not able to cooperate for clinical examination or optical coherence tomography (OCT) investigation will be excluded
Trial locations and contacts
2 sites in 1 country.
Hong Kong2 sites, 2 recruiting
- Recruiting
Southern District Wah Kwai Community Centre
Aberdeen
Jordy Lau, 28315367, jordylau@hku.hk
- Recruiting
Kwun Tong District Health Centre
Kwun Tong
Jordy Lau, 28315367, jordylau@hku.hk
About this study
Official title: Glaucoma Screening With Artificial Intelligence - A Randomized Clinical Trial Comparing Retinal Nerve Fiber Layer Optical Texture Analysis and Optic Disc Photography Assessment
This randomized clinical trial aims to compare the diagnostic performance of two AI-enabled screening strategies - ROTA (RNFL optical texture analysis) assessment versus optic disc photography - in detecting glaucoma within a population-based sample. Secondary objectives are to (1) compare the diagnostic performance of ROTA AI assessment versus OCT RNFL thickness assessment by AI, and ROTA AI assessment versus OCT RNFL thickness assessment by trained graders, (2) investigate the cost-effectiveness of AI ROTA assessment for glaucoma screening, and (3) estimate the prevalence of glaucoma in Hong Kong.
Read the detailed description
Glaucoma is the leading cause of irreversible blindness affecting 76 million patients worldwide in 2020. Characterized by progressive degeneration of the optic nerve, early detection of disease deterioration with timely intervention is critical to prevent progressive loss in vision. In the 5th World Glaucoma Association Consensus Meeting, a diverse and representative group of glaucoma clinicians and scientists deliberated on the value and methods of glaucoma screening. Whereas it has been recognized that early detection of glaucoma for treatment is beneficial to preserve the quality of vision and quality of life as glaucoma treatments are often effective, easy to use and well tolerated, the optimal screening strategy for glaucoma has not yet been determined.
ROTA (Retinal Nerve Fiber Layer Optical Texture Analysis) is a patented algorithm designed to detect axonal fiber bundle loss in glaucoma. Unlike conventional Optical Coherence Tomography (OCT) analysis, ROTA uses non-linear transformation to reveal the optical textures and trajectories of axonal fiber bundles, allowing for intuitive and reliable recognition of RNFL abnormalities without the need for normative databases. It can be applied across different OCT models and is particularly effective at detecting focal RNFL defects in early glaucoma and varying degrees of RNFL damage in end-stage glaucoma. The proposed study will address whether the application AI on ROTA is feasible and cost-effective in the setting of glaucoma screening, and whether ROTA would outperform optic disc photography and OCT RNFL thickness assessment.
- Conditions
- Glaucoma
- Being tested
- ROTA assessment by AI, Optic disc assessment by AI
- Keywords
- Ophthalmology, Glaucoma, Artificial Intelligence, Retinal Nerve Fiber Layer Optical Texture Analysis, Optic Disc Photography Assessment
- Medical subject headings
- Glaucoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ROTA assessment by AIDiagnostic test
The RNFL is imaged with OCT for ROTA and the data are analyzed with a deep learning model.
Optic disc assessment by AIDiagnostic test
The optic disc is imaged with color fundus camera and the data are analyzed with a deep learning model.
| Study group | Role | What they receive |
|---|---|---|
| Retinal nerve fiber layer optical texture analysis (ROTA) | Experimental | The RNFL is imaged with OCT for ROTA. Receives: Diagnostic Test: ROTA assessment by AI |
| Optic disc photography | Active comparator | The optic disc is imaged with color fundus camera. Receives: Diagnostic Test: Optic disc assessment by AI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance for detection of glaucoma
The area under the receiver operating characteristic curve (AUC) for detection of glaucoma
Measured: up to ~1 year
Secondary outcomes (2)
Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma
ICER for glaucoma screening measured by incremental cost per true positive case detected, incremental cost per incremental QALY
Measured: up to ~1 year
The prevalence of glaucoma
Proportion of patients with glaucoma
Measured: up to ~1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Aug 2023
- Study start26 Aug 2023
- Primary completion25 Aug 2024 (estimated)
- Study completion25 Feb 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (23)Papers the sponsor has linked to this study.
- Other trials for GlaucomaStudies of the same condition, open and under way.
- More trials run by The University of Hong KongThe 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- This is a randomized clinical trial with the primary objective to compare the diagnostic performance of two screening strategies - Retinal nerve fiber layer Optical Texture Analysis (ROTA) assessment by Artificial Intelligence (AI) versus (vs.) optic disc photography assessment by AI or trained graders - for detection of glaucoma in a population-based sample.
- Masking
- None (open label)
- Enrolment
- 3,175 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- Orbis
- Responsible party
- Principal investigator, Professor Christopher K.S. Leung, The University of Hong Kong
- Investigators
- Christopher Leung, Principal investigator, The University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06012058
- Sponsor's protocol ID
- H012_Protocol_Glaucoma
- Status last verified
- Sep 2023
- First submitted
- 21 Aug 2023
- Last updated
- 21 Sep 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Flaxman SR, Bourne RRA, Resnikoff S, Ackland P, Braithwaite T, Cicinelli MV, Das A, Jonas JB, Keeffe J, Kempen JH, Leasher J, Limburg H, Naidoo K, Pesudovs K, Silvester A, Stevens GA, Tahhan N, Wong TY, Taylor HR; Vision Loss Expert Group of the Global Burden of Disease Study. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob Health. 2017 Dec;5(12):e1221-e1234. doi: 10.1016/S2214-109X(17)30393-5. Epub 2017 Oct 11.
PubMed 29032195Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014 Nov;121(11):2081-90. doi: 10.1016/j.ophtha.2014.05.013. Epub 2014 Jun 26.
PubMed 24974815Weinreb RN, Leung CK, Crowston JG, Medeiros FA, Friedman DS, Wiggs JL, Martin KR. Primary open-angle glaucoma. Nat Rev Dis Primers. 2016 Sep 22;2:16067. doi: 10.1038/nrdp.2016.67.
PubMed 27654570Kim JS, Ishikawa H, Sung KR, Xu J, Wollstein G, Bilonick RA, Gabriele ML, Kagemann L, Duker JS, Fujimoto JG, Schuman JS. Retinal nerve fibre layer thickness measurement reproducibility improved with spectral domain optical coherence tomography. Br J Ophthalmol. 2009 Aug;93(8):1057-63. doi: 10.1136/bjo.2009.157875. Epub 2009 May 7.
PubMed 19429591Leung CK, Cheung CY, Weinreb RN, Qiu Q, Liu S, Li H, Xu G, Fan N, Huang L, Pang CP, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a variability and diagnostic performance study. Ophthalmology. 2009 Jul;116(7):1257-63, 1263.e1-2. doi: 10.1016/j.ophtha.2009.04.013. Epub 2009 May 22.
PubMed 19464061Pierro L, Gagliardi M, Iuliano L, Ambrosi A, Bandello F. Retinal nerve fiber layer thickness reproducibility using seven different OCT instruments. Invest Ophthalmol Vis Sci. 2012 Aug 31;53(9):5912-20. doi: 10.1167/iovs.11-8644.
PubMed 22871835Leung CK, Lam S, Weinreb RN, Liu S, Ye C, Liu L, He J, Lai GW, Li T, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: analysis of the retinal nerve fiber layer map for glaucoma detection. Ophthalmology. 2010 Sep;117(9):1684-91. doi: 10.1016/j.ophtha.2010.01.026. Epub 2010 Jul 21.
PubMed 20663563Leung CK, Choi N, Weinreb RN, Liu S, Ye C, Liu L, Lai GW, Lau J, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: pattern of RNFL defects in glaucoma. Ophthalmology. 2010 Dec;117(12):2337-44. doi: 10.1016/j.ophtha.2010.04.002. Epub 2010 Aug 3.
PubMed 20678802Leung CK, Yu M, Weinreb RN, Lai G, Xu G, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: patterns of retinal nerve fiber layer progression. Ophthalmology. 2012 Sep;119(9):1858-66. doi: 10.1016/j.ophtha.2012.03.044. Epub 2012 Jun 5.
PubMed 22677426Xu G, Weinreb RN, Leung CKS. Retinal nerve fiber layer progression in glaucoma: a comparison between retinal nerve fiber layer thickness and retardance. Ophthalmology. 2013 Dec;120(12):2493-2500. doi: 10.1016/j.ophtha.2013.07.027. Epub 2013 Sep 17.
PubMed 24053994Xu G, Weinreb RN, Leung CK. Optic nerve head deformation in glaucoma: the temporal relationship between optic nerve head surface depression and retinal nerve fiber layer thinning. Ophthalmology. 2014 Dec;121(12):2362-70. doi: 10.1016/j.ophtha.2014.06.035. Epub 2014 Aug 6.
PubMed 25108319Oddone F, Lucenteforte E, Michelessi M, Rizzo S, Donati S, Parravano M, Virgili G. Macular versus Retinal Nerve Fiber Layer Parameters for Diagnosing Manifest Glaucoma: A Systematic Review of Diagnostic Accuracy Studies. Ophthalmology. 2016 May;123(5):939-49. doi: 10.1016/j.ophtha.2015.12.041. Epub 2016 Feb 15.
PubMed 26891880Biswas S, Lin C, Leung CK. Evaluation of a Myopic Normative Database for Analysis of Retinal Nerve Fiber Layer Thickness. JAMA Ophthalmol. 2016 Sep 1;134(9):1032-9. doi: 10.1001/jamaophthalmol.2016.2343.
PubMed 27442185Leung CK, Mohamed S, Leung KS, Cheung CY, Chan SL, Cheng DK, Lee AK, Leung GY, Rao SK, Lam DS. Retinal nerve fiber layer measurements in myopia: An optical coherence tomography study. Invest Ophthalmol Vis Sci. 2006 Dec;47(12):5171-6. doi: 10.1167/iovs.06-0545.
PubMed 17122099Leung CKS, Lam AKN, Weinreb RN, Garway-Heath DF, Yu M, Guo PY, Chiu VSM, Wan KHN, Wong M, Wu KZ, Cheung CYL, Lin C, Chan CKM, Chan NCY, Kam KW, Lai GWK. Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer. Nat Biomed Eng. 2022 May;6(5):593-604. doi: 10.1038/s41551-021-00813-x. Epub 2022 Jan 6.
PubMed 34992272
Study at a glance
From the registry record, last updated by the sponsor on 21 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Glaucoma Screening With Artificial Intelligence - A Randomized Clinical Trial Comparing Retinal Nerve Fiber Layer Optical Texture Analysis and Optic Disc Photography Assessment
- ClinicalTrials.gov ID
- NCT06012058
- Acronym / sponsor ID
- H012_Protocol_Glaucoma
- Conditions
- Glaucoma
- Keywords
- Ophthalmology, Glaucoma, Artificial Intelligence, Retinal Nerve Fiber Layer Optical Texture Analysis, Optic Disc Photography Assessment
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 3,175 participants (estimated)
- Lead sponsor
- The University of Hong KongUniversity, hospital or other
- Collaborators
- Orbis
- Study start
- 26 Aug 2023 (actual)
- Primary completion
- 25 Aug 2024 (estimated)
- Study completion
- 25 Feb 2025 (estimated)
- First posted
- 25 Aug 2023 (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.
This randomized clinical trial aims to compare the diagnostic performance of two AI-enabled screening strategies - ROTA (RNFL optical texture analysis) assessment versus optic disc photography - in detecting glaucoma within a population-based sample. Secondary objectives are to (1) compare the diagnostic performance of ROTA AI assessment versus OCT RNFL thickness assessment by AI, and ROTA AI assessment versus OCT RNFL thickness assessment by trained graders, (2) investigate the cost-effectiveness of AI ROTA assessment for glaucoma screening, and (3) estimate the prevalence of glaucoma in Hong Kong.
Detailed description
Glaucoma is the leading cause of irreversible blindness affecting 76 million patients worldwide in 2020. Characterized by progressive degeneration of the optic nerve, early detection of disease deterioration with timely intervention is critical to prevent progressive loss in vision. In the 5th World Glaucoma Association Consensus Meeting, a diverse and representative group of glaucoma clinicians and scientists deliberated on the value and methods of glaucoma screening. Whereas it has been recognized that early detection of glaucoma for treatment is beneficial to preserve the quality of vision and quality of life as glaucoma treatments are often effective, easy to use and well tolerated, the optimal screening strategy for glaucoma has not yet been determined.
ROTA (Retinal Nerve Fiber Layer Optical Texture Analysis) is a patented algorithm designed to detect axonal fiber bundle loss in glaucoma. Unlike conventional Optical Coherence Tomography (OCT) analysis, ROTA uses non-linear transformation to reveal the optical textures and trajectories of axonal fiber bundles, allowing for intuitive and reliable recognition of RNFL abnormalities without the need for normative databases. It can be applied across different OCT models and is particularly effective at detecting focal RNFL defects in early glaucoma and varying degrees of RNFL damage in end-stage glaucoma. The proposed study will address whether the application AI on ROTA is feasible and cost-effective in the setting of glaucoma screening, and whether ROTA would outperform optic disc photography and OCT RNFL thickness assessment.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- This is a randomized clinical trial with the primary objective to compare the diagnostic performance of two screening strategies - Retinal nerve fiber layer Optical Texture Analysis (ROTA) assessment by Artificial Intelligence (AI) versus (vs.) optic disc photography assessment by AI or trained graders - for detection of glaucoma in a population-based sample.
| Arm | Type | Receives |
|---|---|---|
| Retinal nerve fiber layer optical texture analysis (ROTA) | Experimental | Diagnostic Test: ROTA assessment by AIThe RNFL is imaged with OCT for ROTA. |
| Optic disc photography | Active comparator | Diagnostic Test: Optic disc assessment by AIThe optic disc is imaged with color fundus camera. |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Individuals aged 50 years or above
Key exclusion criteria (2 listed)
- Physically incapacitated
- Not able to cooperate for clinical examination or optical coherence tomography (OCT) investigation will be excluded
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 |
|---|---|---|
| ROTA assessment by AIThe RNFL is imaged with OCT for ROTA and the data are analyzed with a deep learning model. | Diagnostic test | Retinal nerve fiber layer optical texture analysis (ROTA) |
| Optic disc assessment by AIThe optic disc is imaged with color fundus camera and the data are analyzed with a deep learning model. | Diagnostic test | Optic disc photography |
Primary outcome
Diagnostic performance for detection of glaucoma
Time frame: up to ~1 year
The area under the receiver operating characteristic curve (AUC) for detection of glaucoma
2 secondary outcomes
Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma
Time frame: up to ~1 year
The prevalence of glaucoma
Time frame: up to ~1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Sep 2023.
- Background: PubMed 29032195Flaxman SR, Bourne RRA, Resnikoff S, Ackland P, Braithwaite T, Cicinelli MV, Das A, Jonas JB, Keeffe J, Kempen JH, Leasher J, Limburg H, Naidoo K, Pesudovs K, Silvester A, Stevens GA, Tahhan N, Wong TY, Taylor HR; Vision Loss Expert Group of the Global Burden of Disease Study. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob Health. 2017 Dec;5(12):e1221-e1234. doi: 10.1016/S2214-109X(17)30393-5. Epub 2017 Oct 11.
- Background: PubMed 24974815Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014 Nov;121(11):2081-90. doi: 10.1016/j.ophtha.2014.05.013. Epub 2014 Jun 26.
- Background: PubMed 27654570Weinreb RN, Leung CK, Crowston JG, Medeiros FA, Friedman DS, Wiggs JL, Martin KR. Primary open-angle glaucoma. Nat Rev Dis Primers. 2016 Sep 22;2:16067. doi: 10.1038/nrdp.2016.67.
- Background: PubMed 19429591Kim JS, Ishikawa H, Sung KR, Xu J, Wollstein G, Bilonick RA, Gabriele ML, Kagemann L, Duker JS, Fujimoto JG, Schuman JS. Retinal nerve fibre layer thickness measurement reproducibility improved with spectral domain optical coherence tomography. Br J Ophthalmol. 2009 Aug;93(8):1057-63. doi: 10.1136/bjo.2009.157875. Epub 2009 May 7.
- Background: PubMed 19464061Leung CK, Cheung CY, Weinreb RN, Qiu Q, Liu S, Li H, Xu G, Fan N, Huang L, Pang CP, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a variability and diagnostic performance study. Ophthalmology. 2009 Jul;116(7):1257-63, 1263.e1-2. doi: 10.1016/j.ophtha.2009.04.013. Epub 2009 May 22.
- Background: PubMed 22871835Pierro L, Gagliardi M, Iuliano L, Ambrosi A, Bandello F. Retinal nerve fiber layer thickness reproducibility using seven different OCT instruments. Invest Ophthalmol Vis Sci. 2012 Aug 31;53(9):5912-20. doi: 10.1167/iovs.11-8644.
- Background: PubMed 20663563Leung CK, Lam S, Weinreb RN, Liu S, Ye C, Liu L, He J, Lai GW, Li T, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: analysis of the retinal nerve fiber layer map for glaucoma detection. Ophthalmology. 2010 Sep;117(9):1684-91. doi: 10.1016/j.ophtha.2010.01.026. Epub 2010 Jul 21.
- Background: PubMed 20678802Leung CK, Choi N, Weinreb RN, Liu S, Ye C, Liu L, Lai GW, Lau J, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: pattern of RNFL defects in glaucoma. Ophthalmology. 2010 Dec;117(12):2337-44. doi: 10.1016/j.ophtha.2010.04.002. Epub 2010 Aug 3.
- Background: PubMed 22677426Leung CK, Yu M, Weinreb RN, Lai G, Xu G, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: patterns of retinal nerve fiber layer progression. Ophthalmology. 2012 Sep;119(9):1858-66. doi: 10.1016/j.ophtha.2012.03.044. Epub 2012 Jun 5.
- Background: PubMed 24053994Xu G, Weinreb RN, Leung CKS. Retinal nerve fiber layer progression in glaucoma: a comparison between retinal nerve fiber layer thickness and retardance. Ophthalmology. 2013 Dec;120(12):2493-2500. doi: 10.1016/j.ophtha.2013.07.027. Epub 2013 Sep 17.
- Background: PubMed 25108319Xu G, Weinreb RN, Leung CK. Optic nerve head deformation in glaucoma: the temporal relationship between optic nerve head surface depression and retinal nerve fiber layer thinning. Ophthalmology. 2014 Dec;121(12):2362-70. doi: 10.1016/j.ophtha.2014.06.035. Epub 2014 Aug 6.
- Background: PubMed 26891880Oddone F, Lucenteforte E, Michelessi M, Rizzo S, Donati S, Parravano M, Virgili G. Macular versus Retinal Nerve Fiber Layer Parameters for Diagnosing Manifest Glaucoma: A Systematic Review of Diagnostic Accuracy Studies. Ophthalmology. 2016 May;123(5):939-49. doi: 10.1016/j.ophtha.2015.12.041. Epub 2016 Feb 15.
- Glaucoma Screening, Consensus Series - 5. Hague, Netherlands: Kugler Publications, 2008.Link provided by the sponsor.
- Consensus series 10 - Diagnosis of primary open angle glaucoma (Kugler Publications, 2016).Link provided by the sponsor.
- Optical Texture Analysis of the Inner Retina (US 20190110681)Link provided by the sponsor.
- 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.
Christopher Leung principal investigator
The University of Hong Kong
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing ROTA assessment by AI and Optic disc assessment by AI in people with Glaucoma, run by The University of Hong Kong at 2 sites in Hong Kong.
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 1 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: status not confirmed, as of the sponsor's last update on 21 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Hong Kong. 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?
Contact Anita Yau (39102673, anitayky@hku.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance for detection of glaucoma (time frame: up to ~1 year). 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 NCT06012058, which also lists 23 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 21 Sep 2023.