A review of deep learning in structure and function in glaucoma

Abstract

The relationship between structural damage and functional loss in glaucoma is of great importance for its diagnosis and management. The functional status is usually assessed through visual field examination, a subjective test that is burdensome and time-consuming. Moreover, it depends on patients’ answers and there is a learning curve until accurate and reliable measurements are possible. Structural assessment, on the other hand, has remarkably improved since the development of optical coherence tomography, a fast test that allows for objective and quantitative analysis of retinal layers. The relationship between the two tests, however, is complex and nonlinear, and is influenced by interindividual variability. Thus, qualitative evaluation or the use of conventional statistics might not be appropriate. In recent years, we have seen a remarkable evolution of artificial intelligence algorithms and deep learning models. These techniques have proved adequate to model such complicated relationships. In this review, we summarize studies that investigate the structure and function relationship in glaucoma making use of artificial intelligence and deep learning, the challenges associated with predicting visual field information from structural measurements, and the strategies adopted to improve their accuracy.