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

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

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.

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