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

Insel Gruppe AG, University Hospital Bern4,800 enrolled

Overview

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

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.

Study Type

OBSERVATIONAL

Enrollment

4,800

Conditions

Keratoconus Eye Diseases Cataract Corneal Ectasia

Interventions

Corneal tomography with Eyestar 900DEVICE

Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas

Corneal tomography with PentacamDEVICE

Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas

Biometry with IOL-MasterDEVICE

Non-invasive biometry for presurgical intraocular lens calculation

Eligibility

Sex: ALLHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: 1. Patients with all stages of keratoconus 2. Patients with healthy corneas Exclusion Criteria: 1. Keratoconus patients with hydrops, status following hydrops 2. Patients with degenerative corneal diseases 3. Patients after corneal surgery

Locations (1)

Universitätsklinik für Augenheilkunde, Inselspital

Bern, Switzerland

Outcomes

Primary Outcomes

Keratoconus identification

Classification accuracy of the keratoconus identification algorithm for the Eyestar device in comparison to the gold standard (Belin-Ambrosio Enhanced Extasia Deviation Index) BAD\_D in Pentacam images.

Time frame: 2.5 years

Secondary Outcomes

Feasibility in clinical practice

Evaluation of the feasibility (percentage of valid measurements without errors and/or problems in image aquisition) of cornea measurements in keratoconus and healthy eyes.

Time frame: 2.5 years

Data from ClinicalTrials.gov

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