Trials / Completed
CompletedNCT04497207
Deep Learning for Classification of Scheimpflug Corneal Tomography Images
Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 1,669 (actual)
- Sponsor
- Assiut University · Academic / Other
- Sex
- All
- Age
- 18 Years – 45 Years
- Healthy volunteers
- Accepted
Summary
Keratoconus is a common disorder. An early diagnosis influences the disease prognosis in the affected patients and prevents postoperative complications in patients with keratoconus considering refractive surgery. Machine learning approaches have been widely used for image classification. Here, we will assess the ability of deep learning to enable high-performance image classification of the color-coded corneal maps obtained by Scheimpflug camera in patients with keratoconus, subclinical keratoconus, and normal individuals.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| OTHER | Scheimpflug Camera Corneal Tomography | Pentacam Sheimpflug system(Pentacam HR, Oculus Optikgeräte GmbH, software V.1.15r4 n7) is used for imaging of the anterir and posterior surfaces of the cornea to obtain corneal tomographic maps. |
Timeline
- Start date
- 2020-08-10
- Primary completion
- 2020-08-20
- Completion
- 2020-08-25
- First posted
- 2020-08-04
- Last updated
- 2020-10-06
Locations
1 site across 1 country: Egypt
Source: ClinicalTrials.gov record NCT04497207. Inclusion in this directory is not an endorsement.