Dry EyeNCT07554911
Detection and Optimization of Treatment of Severe Cases of Dry Eye Disease
An observational study of Dry Eye, run by Singapore National Eye Centre at 1 site in Singapore.
Trial summary
The bulk of dry eye patients are found in the community. The lack of satisfactory protocols and confidence is a significant deterrent for practitioners to manage such patients, which may result in inaccurate referrals, and unhappy patients. Read more about this study
- Who is it for?
- Ages 21 to 99 yearsHealthy volunteers can take part
- Conditions the study is for
- Dry Eye
- How many people
- 200 participants
- Where
- 1 siteSingapore
- Study dates
- 18 Oct 2023to 31 Jul 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 21 to 99 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: dry eye patients
Who may be able to join
- 21 years old and above
- Participants must be previously diagnosed with dry eye in the dry eye clinic (previous referred and had various forms of treatment such as artificial tears or prescription eyedrops)
- Willing to perform all eye examinations and questionnaires in this study
- Ability to provide informed consent
Who cannot join
- All subjects meeting any of the exclusion criteria at baseline will be excluded from participation and then list the criterion.
- Any other specified reason as determined by clinical investigator
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Singapore1 site, 1 recruiting
- Recruiting
Singapore Eye Research Institute
Singapore, 169856
Sharon Yeo, BSc, 65767200, sharon.yeo.w.j@seri.com.sg
About this study
The bulk of dry eye patients are found in the community. The lack of satisfactory protocols and confidence is a significant deterrent for practitioners to manage such patients, which may result in inaccurate referrals, and unhappy patients. Problems are compounded by comorbidities of dry eye, even if these are not diagnosed formally.
Aligning with the healthcare strategy to move beyond healthcare to health, and beyond hospital care to community care, investigators propose that the confidence of primary carers be increased by using an image-based screening system.
This study aim to determine the efficacy of this screening AI algorithm, a prototype, in addition to or instead of screening of dry eye using a simple DEQ-5 symptom questionnaire.
Read the detailed description
Investigators have shown that a single corneal picture after dye staining can detect DED that are ideally managed at tertiary care because these require prescription eyedrops. The main type of DED patients that respond to cyclosporine eyedrops are those with severe cornea staining. In collaboration with data scientists from ASTAR, the preliminary data involving more than 1000 images from China and Singapore show that this artificial intelligence-based screening is sensitive and specific.
By reducing unnecessary referrals to hospitals, investigators will make healthcare more sustainable and affordable. Previously, patients in the community are evaluated purely based on subjective symptoms. investigators not only standardize this with a validated and short DEQ5 questionnaire, but evaluate the accuracy of screening is improved by using the AI algorithms on the corneal image, a prototype, in addition to the DEQ5, and in place of the DEQ5.
Aim: Determine the efficacy of this screening AI algorithm, a prototype, in addition to or instead of screening of dry eye using a simple DEQ-5 symptom questionnaire.
Rationale: DEQ-5 is aimed to detect dry eye cases, but not necessarily dry eye requiring specialist care. The AI algorithm picks up cases with central cornea staining, which can then be referred for specialist care. Non-referred cases can be managed with eyelid warming, artificial tears and advice, with the aim of rescreening at a later time.
- Conditions
- Dry Eye
- Keywords
- dry eye
- Medical subject headings
- Dry Eye Syndromes
What is being measured
The primary outcome is the main question the study is designed to answer.
Determine the efficacy of this screening AI algorithm, a prototype, in addition to or instead of screening of dry eye using a simple DEQ-5 symptom questionnaire.
DEQ-5 is aimed to detect dry eye cases, but not necessarily dry eye requiring specialist care. The AI algorithm picks up cases with central cornea staining, which can then be referred for specialist care. Non-referred cases can be managed with eyelid warming, artificial tears and advice, with the aim of rescreening at a later time.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Apr 2026
- Study start18 Oct 2023
- Primary completion31 Jul 2028 (estimated)
- Study completion31 Jul 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Dry EyeStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Singapore National Eye Centre
- Sponsor type
- Other government
- Responsible party
- Sponsor
- Investigators
- Louis Tong, Principal investigator, Singapore Eye Research Institute (SERI)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07554911
- Sponsor's protocol ID
- 2023-2479
- Other IDs
- MOH-001363-00
- Status last verified
- Apr 2026
- First submitted
- 21 Apr 2026
- Last updated
- 1 May 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection and Optimization of Treatment of Severe Cases of Dry Eye Disease
- ClinicalTrials.gov ID
- NCT07554911
- Acronym / sponsor ID
- 2023-2479
- Conditions
- Dry Eye
- Keywords
- dry eye
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Singapore National Eye CentreOther government
- Study start
- 18 Oct 2023 (actual)
- Primary completion
- 31 Jul 2028 (estimated)
- Study completion
- 31 Jul 2028 (estimated)
- First posted
- 28 Apr 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.
The bulk of dry eye patients are found in the community. The lack of satisfactory protocols and confidence is a significant deterrent for practitioners to manage such patients, which may result in inaccurate referrals, and unhappy patients. Problems are compounded by comorbidities of dry eye, even if these are not diagnosed formally.
Aligning with the healthcare strategy to move beyond healthcare to health, and beyond hospital care to community care, investigators propose that the confidence of primary carers be increased by using an image-based screening system.
This study aim to determine the efficacy of this screening AI algorithm, a prototype, in addition to or instead of screening of dry eye using a simple DEQ-5 symptom questionnaire.
Detailed description
Investigators have shown that a single corneal picture after dye staining can detect DED that are ideally managed at tertiary care because these require prescription eyedrops. The main type of DED patients that respond to cyclosporine eyedrops are those with severe cornea staining. In collaboration with data scientists from ASTAR, the preliminary data involving more than 1000 images from China and Singapore show that this artificial intelligence-based screening is sensitive and specific.
By reducing unnecessary referrals to hospitals, investigators will make healthcare more sustainable and affordable. Previously, patients in the community are evaluated purely based on subjective symptoms. investigators not only standardize this with a validated and short DEQ5 questionnaire, but evaluate the accuracy of screening is improved by using the AI algorithms on the corneal image, a prototype, in addition to the DEQ5, and in place of the DEQ5.
Aim: Determine the efficacy of this screening AI algorithm, a prototype, in addition to or instead of screening of dry eye using a simple DEQ-5 symptom questionnaire.
Rationale: DEQ-5 is aimed to detect dry eye cases, but not necessarily dry eye requiring specialist care. The AI algorithm picks up cases with central cornea staining, which can then be referred for specialist care. Non-referred cases can be managed with eyelid warming, artificial tears and advice, with the aim of rescreening at a later time.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 21 to 99 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- dry eye patients
Key inclusion criteria (4 listed)
- 21 years old and above
- Participants must be previously diagnosed with dry eye in the dry eye clinic (previous referred and had various forms of treatment such as artificial tears or prescription eyedrops)
- Willing to perform all eye examinations and questionnaires in this study
- Ability to provide informed consent
Key exclusion criteria (2 listed)
- All subjects meeting any of the exclusion criteria at baseline will be excluded from participation and then list the criterion.
- Any other specified reason as determined by clinical investigator
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Determine the efficacy of this screening AI algorithm, a prototype, in addition to or instead of screening of dry eye using a simple DEQ-5 symptom questionnaire.
Time frame: 3 years
DEQ-5 is aimed to detect dry eye cases, but not necessarily dry eye requiring specialist care. The AI algorithm picks up cases with central cornea staining, which can then be referred for specialist care. Non-referred cases can be managed with eyelid warming, artificial tears and advice, with the aim of rescreening at a later time.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 May 2026.
Related trials for Dry Eye
Not yet recruitingPhase 1
R-2487 in Patients With Sjogren's Syndrome (SjD)
Sjogren's Syndrome, Sjögren
RecruitingPhase 1
A Trial in Healthy Adult Participants and Adults With Autoimmune Diseases to Test How HBM7020 is Tolerated and How it Behaves in the Body
Rheumatoid Arthritis, Systemic Lupus Erythematosus
RecruitingObservational study
Pain Phenotyping of Rheumatic Diseases - An Observational Study
Rheumatic Diseases, Lupus Erythematosus, Systemic
RecruitingPhase 1/2
Autologous Transplantation of Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium for Geographic Atrophy Associated With Age-Related Macular Degeneration
Dry Age-Related Macular Degeneration, Geographic Atrophy
RecruitingObservational study
The Pathogenesis and Natural History of Sjogren's Disease
Sjogren's Syndrome, Salivary Gland
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.
Louis Tong principal investigator
Singapore Eye Research Institute (SERI)
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 Dry Eye, run by Singapore National Eye Centre at 1 site in Singapore.
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 4 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: looking for participants now, as of the sponsor's last update on 1 May 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 Singapore. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sharon Yeo, BSc (65767200, sharon.yeo.w.j@singhealth.com.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Determine the efficacy of this screening AI algorithm, a prototype, in addition to or instead of screening of dry eye using a simple DEQ-5 symptom questionnaire. (time frame: 3 years). 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 May 2026.