Computer-aided medical image analysis has advantages, but requires large amounts of training data, which are scarce and costly to obtain, are subject to privacy concerns, and are often highly imbalanced, with over-representation of common conditions and poor representation of rare conditions. Consequently, some methods have been proposed to generate artificial medical images using generative adversarial networks (GANs). Computer aided diagnosis of keratoconus is an emerging research field that may benefit greatly from medical image synthesis, which can affordably provide an arbitrary number of sufficiently diverse synthetic images that mimic real Pentacam images. A new conditional GAN, the pix2pix cGAN, has not been used in this context to date. Here, investigators will assess the efficacy of a cGAN implementing pix2pix image translation for image synthesis of color-coded Pentacam 4-map refractive displays of clinical and subclinical keratoconus as well as normal corneas.
Study Type
OBSERVATIONAL
Enrollment
923
Pentacam Sheimpflug system(Pentacam HR, Oculus Optikgeräte GmbH, software V.1.15r4 n7) is used for imaging of the anterior and posterior surfaces of the cornea to obtain corneal tomographic maps.
Faculty of Medicine
Asyut, Egypt
Scheimpflug camera color-coded corneal tomography images created by pix2pix conditional adversarial network (pix2pix cGAN)
Time frame: 1 day
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