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Deep Learning for Classification of Scheimpflug Corneal Tomography Images

NCT04497207 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1669

Last updated 2020-10-06

No results posted yet for this study

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

  • Eye Diseases

Interventions

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.

Sponsors & Collaborators

  • Assiut University

    lead OTHER

Eligibility

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

Timeline & Regulatory

Start
2020-08-10
Primary Completion
2020-08-20
Completion
2020-08-25

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 NCT04497207 on ClinicalTrials.gov