Primary open‐angle glaucoma is a leading cause of blindness worldwide.1 Glaucomatous changes to the optic nerve head and retinal nerve fibre layer (RNFL) loss often precede achromatic visual field defects, which may become manifest only after a large percentage of retinal ganglion cells have been damaged.2,3 As such, early disease detection may be important in disease management strategies. Confocal scanning laser ophthalmoscopy has become an important tool for detecting structural damage of the optic nerve head and RNFL, and may assist in early glaucoma detection.4,5 There seem to be important racial differences between people of African and European ancestry regarding optic disc configuration6,7,8,9,10,11 and neuroretinal rim area.12,13 Given these variations, questions exist as to whether the current Heidelberg Retinal Tomograph (HRT)‐II database of people of European ancestry can be applied to other racial groups.10 Heidelberg retinal tomography (Heidelberg Engineering, GmBH, Dossenheim, Germany) uses confocal scanning laser technology to calculate topographic measurements of the optic nerve and parapapillary RNFL. One method of the HRT‐II software analysis, Moorfields regression analysis (MRA), uses an algorithm to compare measured optic nerve parameters with those from a normative database.9 Although there is good reproducibility of HRT measurements,14,15,16 a major limitation of this device is the need for an operator to draw a contour line at the border of the optic disc. This can result in variability in measurements between different observers.17 The HRT‐II uses a normative database consisting of 349 normal eyes of white people, whereas HRT‐III uses an enlarged race‐specific database, consisting of eyes of 733 white and 215 black people (Heidelberg Engineering, personal communication, Heidelberg Engineering, 2006). HRT‐III software V.3.0 includes the calculation of the Glaucoma Probability Score (GPS), a new, automated algorithm that evaluates both optic disc and parapapillary RNFL topography to estimate the probability of having glaucoma. The GPS uses two measures of parapapillary RNFL shape (horizontal and vertical RNFL curvature) and three measures of optic nerve head shape (cup size, cup depth and rim steepness) for input into a vector machine‐learning classifier that estimates the probability of having damage consistent with glaucoma. No contour line or reference plane is used in the GPS calculation, and therefore the analysis is operator independent. This is based on mathematical modelling of the optic nerve shape, which typically exhibits a cup with varying width and depth, as well as curvature of the rim region.18 The normally convex RNFL curvature, caused by the ganglion cell axons converging towards the optic nerve, flattens as axons are lost as a result of glaucoma. In the present study, we compared the abilities of GPS and MRA to differentiate between glaucomatous and normal eyes using HRT‐III software and race‐specific databases.