NCT04763785

Development of a Keratoconus Detection Algorithm by Deep Learning Analysis and Its Validation on Eyestar Images

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 4,800 (estimated)
• Sponsor: Insel Gruppe AG, University Hospital Bern · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

Summary

Monocentric clinical study to develop an imaging analysis algorithm for the Eyestar 900 to identify keratoconus corneas and improve biometry for intraocular lens calculations

Detailed 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 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.

Conditions

• Keratoconus
• Eye Diseases
• Cataract
• Corneal Ectasia

Interventions

Type	Name	Description
DEVICE	Corneal tomography with Eyestar 900	Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas
DEVICE	Corneal tomography with Pentacam	Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas
DEVICE	Biometry with IOL-Master	Non-invasive biometry for presurgical intraocular lens calculation
OTHER	retrospective analysis, no intervention	retrospective analysis of 4500 existing, fully anonymised picture data

Timeline

• Start date: 2021-05-11
• Primary completion: 2023-06-01
• Completion: 2023-12-01
• First posted: 2021-02-21
• Last updated: 2021-09-30

Locations

1 site across 1 country: Switzerland