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Pix2pix Networks for Scheimpflug Corneal Tomography Image Generation

NCT04582253 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 923

Last updated 2020-10-19

No results posted yet for this study

Summary

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.

Conditions

  • Eye Diseases

Interventions

DIAGNOSTIC_TEST

Scheimpflug Camera Corneal Tomography

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.

Sponsors & Collaborators

  • Assiut University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
50 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-08-01
Primary Completion
2020-08-20
Completion
2020-10-01

Countries

  • Egypt

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04582253 on ClinicalTrials.gov