Health Condition 1: H400- Glaucoma suspect
Conditions
Interventions
Intervention1: Nil: Nil
Control Intervention1: Nil: Nil
Sponsors
National University of Singapore
Eligibility
Inclusion criteria
Inclusion criteria: 1. All ethnicities 2. All refractive errors 3. All ophthalmic conditions/pathologies 4. All visual fields and 5. All other conditions (e.g. Alzheimer).
Exclusion criteria
Exclusion criteria: 1. We will exclude subjects in which the ONH tissues (neural and connective) are not visible with OCT, as assessed manually.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| To identify whether deep learning (artificial intelligence) can provide better diagnostic power than that derived from any other glaucoma index. If so, we would like to predict the diagnosis and the risk factor for the patients for glaucoma merely from OCT images. Time: 2 yrs (1 yr data collection + 6 months training and testing of the algorithm + 6 months validation)Timepoint: 24 months | — |
Secondary
| Measure | Time frame |
|---|---|
| "To compare the performance of deep learning based diagnosis from OCT images against other clinicial methods currently being used. Timepoint: 06 Months | — |
Countries
Australia, China, India, Philippines, Russian Federation, Singapore, United Arab Emirates, United States of America
Contacts
Public ContactDr SR Krishnadas
Aravind Eye Hospital and PG Institute of Ophthalmology
Outcome results
None listed