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Completed

NCT Number: NCT04497207

Deep Learning for Classification of Scheimpflug Corneal Tomography Images

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.

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Key information

Conditions

Eye Diseases Corneal Diseases Keratoconus

Age range

18 year–45 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Faculty of Medicine

Asyut, 71515, Egypt

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Keratoconus group:

  • Presence of a central protrusion of the cornea with Fleischer ring, Vogt striae, or both by slitlamp examination.
  • irregular cornea determined by distorted keratometry mires and distortion of retinoscopic red reflex or both in addition to the following topographic findings as summarized by Pińero and colleagues :
  • focal steepening located in a zone of protrusion surrounded by concentrically decreasing power zones
  • focal areas with diopteric (D) values >47.0D
  • inferior- superior(I-S) asymmetry measured to be > 1.4 D
  • Angling of the hemimeridians in an asymmetric or broken bowtie pattern with skewing of the steepest radial axis(SRAX) .

Suspicious group:

  • Defined as subtle corneal tomographic changes as the aforementioned keratoconus abnormalities in the absence of slit- lamp or visual acuity changes typical of keratoconus (forme fruste keratoconus).

Normal group:

  • Refractive surgery candidates
  • Refractive error of less than 8.0 D sphere
  • Less than 3.0 D of astigmatism
  • without clinical, topographic or tomographic signs of keratoconus or suspect keratoconus.

Exclusion criteria

  • Systemic disease
  • Other corneal disease such as pellucid marginal degeneration
  • History of trauma
  • Corneal surgery such as corneal cross- linking for progressive keratoconus.

Treatment and study plan

Scheimpflug Camera Corneal Tomography

Other

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.

Primary outcomes

  1. Croppedm denoised, and resized to obtain four separate image stacks of 256×256 pixels

    Time frame: 2 days

Sponsors and collaborators

Lead sponsor

Assiut University

Other

Registry information

Official study title

Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning

Important dates

Study start
2020
Primary completion
2020
Study completion
2020
First posted
Aug 4, 2020
Registry last updated
Oct 6, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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