Development of a Keratoconus Detection Algorithm by Deep Learning Analysis and Its Validation on Eyestar Images
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Cataract
- Corneal Ectasia
- Eye Diseases
- Keratoconus
- Type
- Observational
- Design
- Observational Model: CohortTime Perspective: Prospective
Participation Requirements
- Age
- Younger than 125 years
- Gender
- Both males and females
Description
Keratoconus is a progressive corneal ectatic disorder, characterised by thinning, protrusion and irregularity. Corneal imaging is crucial in keratoconus detection and progression analysis. Detection of keratoconus in early stages is important and has therapeutic consequence, whether to plan a surgic...
Keratoconus is a progressive corneal ectatic disorder, characterised by thinning, protrusion and irregularity. Corneal imaging is crucial in keratoconus detection and progression analysis. Detection of keratoconus in early stages is important and has therapeutic consequence, whether to plan a surgical intervention or calculating an intraocular lens, before cataract surgery, as standard lens calculation techniques may lead to wrong results in patients with a keratoconus. The Eyestar 900 is a swept-source OCT biometer and has the potential to be used for early keratoconus identification and progression analysis.
Tracking Information
- NCT #
- NCT04763785
- Collaborators
- Not Provided
- Investigators
- Principal Investigator: Christoph Tappeiner, Prof. Dr. med. Universitätsklinik für Augenheilkunde, Inselspital Bern
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