Development of an IT Tool Able to Identify Ocular Conditions

Overview

The aim of the study is to use machine learning to develop an IT tool able to differentiate between eye conditions analysing corneal biomechanical data.

Data will be collected using two different commercially available devices that are able to measure corneal biomechanics. Corneal biomechanics will be measured in participants with different conditions: glaucoma, ocular hypertension, corneal conditions, and healthy controls as it is well established that the above-mentioned conditions cause changes in corneal biomechanical properties. Corneal biomechanics are the mechanical properties of the cornea, as rigidity, elasticity and it is possible to measure them using two devices: Ocular Response Analyzer (ORA) or Corneal Visualization Scheimpflug Technology (Corvis ST). Both devices use a puff of air to temporally flatten the cornea and derive the properties of the tissue. Participants with ocular conditions will be recruited at Birmingham and Midlands Eye Centre (BMEC) at the Glaucoma and Anterior Eye clinics among patients attending for their routine clinical appointment. Healthy controls will be recruited at Aston University. This study requires only one visit and there is no need of follow up. A portion of the data collected will be used to train machine learning algorithms to differentiate between conditions, the remaining data will be used to test the accuracy of newly created algorithms. The algorithm will be developed using Orange Data Mining.

Conditions

Interventions

Eligibility

Outcomes