A Machine Learning Approach for a Vision-Based Van-Herick Measurement System

Abstract

The application of Artificial Intelligence to the instrumentation and measurements field is nowadays an attractive research area. Indeed, Artificial Intelligence gives the possibility to perform activities also in case of inability to perfectly model a phenomenon or a system. Furthermore, making machines learn from data how to perform an activity, rather than hard code sequential instructions, is a common and effective practice in many modern research areas. This paper investigates the possibility to use Machine Learning techniques in an ophthalmic vision–based system performing automatic Anterior Chamber Angle measurements. Currently, this procedure can be performed only by appropriately trained medical personnel. For this reason, Machine Learning and Vision–Based techniques may greatly improve both test objectiveness and diagnostic accessibility, by allowing to automatically carry out the measurement procedure.