The ability of the GDx Nerve Fibre Analyser neural network to diagnose glaucoma

Abstract.

Purpose: To evaluate the neural network used by the GDx in a group of normal subjects, patients with ocular hypertension (OHT) and patients with normal-pressure glaucoma (NPG). Methods: The GDx neural network produces a "number" that indicates the likelihood that glaucoma is present. This number was compared in three groups representing different stages of health and disease, namely, normal controls (n=101), OHT (n=102) and NPG (105). The GDx number's ability to differentiate between normal and glaucoma individuals was then investigated. We also studied the relationship between the GDx number and retinal nerve fibre layer (RNFL) average thickness and visual field status to examine how well the GDx number reflects disease severity. Results: The GDx number was significantly different among the groups (P<0.01); it was highest in NPG and lowest in normal controls. The GDx number differentiated between glaucoma and normal with sensitivity of 92.3% and specificity of 96%. When combined with the parameter of RNFL average thickness, sensitivity and specificity were 88.5% and 100% respectively. In NPG a significant correlation was found between the GDx number and RNFL average thickness(rho=–0.88, P<0.001) and visual field mean deviation (rho=–0.64, P<0.001). Conclusion: The GDx number is able to differentiate between groups of normal, OHT and NPG subjects. Its close relationship with RNFL average thickness and visual field status in glaucoma indicates that it is able to reflect disease severity. Furthermore, its measured ability to distinguish between normal individuals and those with glaucoma demonstrates potential for use in glaucoma diagnosis.