Validation of an Objective Keratoconus Detection System Implemented in a Scheimpflug Tomographer and Comparison With Other Methods.

Purpose: To validate a recently developed program for automatic and objective keratoconus detection (Keratoconus Assistant [KA]) by applying it to a new population and comparing it with other methods described in the literature.

Methods: KA uses machine learning and 25 Pentacam-derived parameters to classify eyes into subgroups, such as keratoconus, keratoconus suspect, postrefractive surgery, and normal eyes. To validate this program, it was applied to 131 eyes diagnosed separately by experienced corneal specialists from 2 different centers (Fondation Rothschild, Paris, and Antwerp University Hospital [UZA]).

SyntEyes KTC: higher order statistical eye model for developing keratoconus.

Purpose: To present and validate a stochastic eye model for developing keratoconus to e.g. improve optical corrective strategies.

Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.

Purpose: To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with other known keratoconus (KC) classification methods.

Methods: Pentacam data from 860 eyes were included in the study and divided into 5 groups: 454 KC, 67 forme fruste (FF), 28 astigmatic, 117 after refractive surgery (PR), and 194 normal eyes (N). Twenty-two parameters were used for classification using a support vector machine algorithm developed in Weka, a machine-learning computer software.

How Abnormal Is the Noncorneal Biometry of Keratoconic Eyes?

Purpose: To determine whether the noncorneal biometry in keratoconic eyes deviates from that in healthy eyes.

Methods: The right eyes of 200 healthy subjects and 76 patients with keratoconus were measured with an autorefractometer, a Scheimpflug tomographer, and an optical biometer. The analysis consisted of a general linear model (GLM), correcting for age and gender effects, comparing keratoconic eyes with healthy eyes, and emmetropic eyes.

Subjective Grading of Subclinical Vitreous Floaters.

Purpose: This study aimed to objectively grade the perception of subclinical floaters in an asymptomatic cohort.

Design: A prospective observational cohort study.

Methods: One hundred eighty-two volunteers (49 men, 133 women) with ages ranging from 17.

Repeatability and inter-device agreement for three different methods of keratometry: Placido, Scheimpflug, and color LED corneal topography.

Purpose: To determine the repeatability of a color LED corneal topographer (Cassini; iOptics, The Hague, The Netherlands) and compare it with Placido and Scheimpflug based devices (EyeSys 2000; EyeSys Laboratories, Houston, TX, and Pentacam HR; Oculus Optikgeräte GmbH, Wetzlar, Germany).

Methods: This prospective study involved 20 healthy volunteers (20 eyes) recruited from the staff of the Antwerp University Hospital. For each eye, three measurements were taken using each device, from which eight parameters describing keratometry and astigmatism were derived.

Normative values for corneal densitometry analysis by Scheimpflug optical assessment.

Purpose: To describe the normative data for corneal Scheimpflug densitometry based on a cohort of normal participants.

Methods: A total of 445 healthy participants were recruited for assessment (794 eyes). Left and right eyes were considered separately.