Detection and Optimization of Treatment of Severe Cases of Dry Eye Disease

April 27, 2026 updated by: Singapore National Eye Centre

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.

Study Overview

Status

Recruiting

Conditions

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 Type

Observational

Enrollment (Estimated)

200

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

dry eye patients

Description

Inclusion Criteria:

  1. 21 years old and above
  2. 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)
  3. Willing to perform all eye examinations and questionnaires in this study
  4. Ability to provide informed consent

Exclusion Criteria:

  1. All subjects meeting any of the exclusion criteria at baseline will be excluded from participation and then list the criterion.
  2. Any other specified reason as determined by clinical investigator

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Singapore National Eye Centre

Investigators

  • Principal Investigator: Louis Tong, Singapore Eye Research Institute (SERI)

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 18, 2023

Primary Completion (Estimated)

July 31, 2028

Study Completion (Estimated)

July 31, 2028

Study Registration Dates

First Submitted

April 21, 2026

First Submitted That Met QC Criteria

April 21, 2026

First Posted (Actual)

April 28, 2026

Study Record Updates

Last Update Posted (Actual)

May 1, 2026

Last Update Submitted That Met QC Criteria

April 27, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2023-2479
  • MOH-001363-00 (Other Grant/Funding Number: Ministry of Health, NMRC, Singapore)

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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