Deep Learning for Classification of Scheimpflug Corneal Tomography Images

Status Completed

Clinical Trial 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.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Eye Diseases

Clinical Trial Details

NCT number	NCT04497207
Study type	Observational
Source	Assiut University
Contact
Status	Completed
Phase
Start date	August 10, 2020
Completion date	August 25, 2020

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