CATALYZE: A DEEP LEARNING APPROCH FOR CATARACT ASSESSEMENT AND GRADING ON SS-OCT ANTERION IMAGES.

Purpose: To assess an new objective deep learning model cataract grading method based on Swept-Source Optical Coherence Tomography (SS-OCT) scans provided by the Anterion® (Heidelberg, Germany).

Setting: Single centre study at the Rothschild Foundation, Paris, France.

Design: Prospective cross-sectional study.

Novel deep learning approach to estimate rigid gas permeable contact lens base curve for keratoconus fitting.

Introduction: Rigid gas permeable contact lenses (RGP) are the most efficient means of providing optimal vision in keratoconus. RGP fitting can be challenging and time-consuming for ophthalmologists and patients. Deep learning predictive models could simplify this process.

Discrimination between keratoconus, forme fruste keratoconus, and normal eyes using a novel OCT-based tomographer.

Purpose: To combine objective machine-derived corneal parameters obtained with new swept-source optical coherence tomography (SS-OCT) tomographer (Anterion) to differentiate between normal (N), keratoconus (KC) and forme fruste KC (FFKC).

Setting: Laser Center, Hôpital Fondation Adolphe de Rothschild, Paris, France.

Design: Retrospective study.

Deep Learning Model for the Detection of Corneal Edema Before Descemet Membrane Endothelial Keratoplasty on Optical Coherence Tomography Images.

Purpose: Descemet membrane endothelial keratoplasty (DMEK) is the preferred method for treating corneal endothelial dysfunction, such as Fuchs endothelial corneal dystrophy (FECD). The surgical indication is based on the patients' symptoms and the presence of corneal edema. We developed an automated tool based on deep learning to detect edema in corneal optical coherence tomography images.