Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis.

PURPOSE: To develop, train, and test an artificial neural network (ANN) for differentiating among normal subjects, primary open angle glaucoma (POAG) suspects, and persons with POAG in Asian-Indian eyes using inputs from clinical parameters, optical coherence tomography (OCT), visual fields, and GDx nerve fiber analyzer.
METHODS: One hundred eyes were classified using optic disc examination and perimetry into normal (n=35), POAG suspects (n=30), and POAG (n=35). EasyNN-plus simulator was used to develop an ANN model with inputs including age, sex, myopia, intraocular pressure (IOP), optic nerve head, and retinal nerve fiber layer (RNFL) parameters on OCT, Octopus 30-2 full threshold visual field, and GDx parameters.
RESULTS: With two outputs (POAG or normal), specificity was 80% and sensitivity was 93.3%. Ninety percent of POAG suspects were labeled as abnormal in this analysis. ANN assigned the highest importance to Smax/Imax RNFL on OCT followed by cup-area (OCT) and other RNFL parameters (OCT) for two outputs. With three outputs (normal, POAG, and POAG suspect), ANN gave an overall classification rate of 65%, specificity of 60%, and sensitivity of 71.4% with a target error rate of the training set at 1%. The parameters for three outputs, in decreasing order of relative importance, were Savg, vertical cup-disc ratio, cup-volume, and cup-area on OCT.
CONCLUSIONS: An ANN taking varied diagnostic imaging inputs was able to separate POAG eyes from normal subjects and POAG suspects. The network had reasonable sensitivity with three outputs; however, it had a tendency to mislabel POAG suspects as POAG.