OCT data of glaucoma patients and normal population for developing deep learning model glaucoma deep learning model optical coherence tomography
Conditions
Interventions
Patients who diagnosed glaucoma by vertical cup to disc ratio more than 0.5 with increased IOP more than 21 mmHg or glaucomatous visual field defect ,Normal subject without glaucoma after complete oph
Diagnostic,Diagnostic
Patients who diagnosed glaucoma ,Normal subject without glaucoma
Sponsors
Faculty of Medicine Siriraj Hospital, Mahidol University
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Inclusion criteria
Inclusion criteria: 1. Patients with age over 18 years old 2. Patients who diagnosed glaucoma 400 eyes 3. Normal subject without glaucoma 100 eyes 4. Each subject should have OCT result and HVF 24-2 result within the same period (not more than 6 months apart)
Exclusion criteria
Exclusion criteria: 1. Patients with ocular comorbidities which obscure OCT data gathering 2. OCT signal strength less than 6 3. Patient with other comorbidities which interfere HVF 24-2 result e.g. retinal disease or neuro-ophthalmologic disease 4. Unreliable HVF result (fixation loss > 20%, false positive > 20%, false negative > 20%, rim artifact, lid effect, clover leaf)
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| efficacy of the model end of study AUC, true positive, true negative, sensitivity, specificity | — |
Secondary
| Measure | Time frame |
|---|---|
| correlation between predicted outcome from the model based on OCT versus HVF 24-2 result end of study comparison of visual field index (VFI) using independent sample T-Test | — |
Countries
Thailand
Contacts
Public ContactDarin Sakiyalak
Faculty of Medicine Siriraj Hospital, Mahidol University
Outcome results
None listed