Modeling Visual Impairments with Artificial Neural Networks: A Review

Abstract

We present an approach to bridge the gap between the computational models of human vision and the clinical practice on visual impairments (VI). In a nutshell, we propose to connect advances in neuroscience and machine learning to study the impact of VI on key functional competencies and improve treatment strategies. We review related literature, with the goal of promoting the full exploitation of Artificial Neural Network (ANN) models in meeting the needs of visually impaired individuals and the operators working in the field of visual rehabilitation. We first summarize the existing types of visual issues, the key functional vision-related tasks, and the current methodologies used for the assessment of both. Second, we explore the ANNs best suitable to model visual issues and to predict their impact on functional vision-related tasks, at a behavioral (including performance and attention measures) and neural level. We provide guidelines to inform the future research about developing and deploying ANNs for clinical applications targeting individuals affected by VI.