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.
Study Type
OBSERVATIONAL
Enrollment
1,669
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.
Faculty of Medicine
Asyut, Egypt
Croppedm denoised, and resized to obtain four separate image stacks of 256×256 pixels
Time frame: 2 days
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.