Artificial Neural Network Approach for Differentiating Open-Angle Glaucoma From Glaucoma Suspect Without a Visual Field Test

Abstract

Purpose: To increase the effectiveness of treating open-angle glaucoma (OAG), we tried to find a screening method of differentiating OAG from glaucoma suspect (GS) without a visual field (VF) test.

Methods: Data were collected from the Korean National Health and Nutrition Examination Survey (KNHANES) conducted in 2010. Of 8958 participants, 386 suspected OAG subjects underwent a VF test. For the training dataset, five OAG risk prediction models were created using multivariate logistic regression and an artificial neural network (ANN) with various clinical variables. Informative variables were selected by an algorithm of consistency subset evaluation, and cross validation was used to optimize performance. The test dataset was used subsequently to assess OAG-prediction performance using the area under the curve (AUC) of the receiver-operating characteristic.

Results: Among five OAG risk prediction models, an ANN model with nine noncategorized factors had the greatest AUC (0.890). It predicted OAG with an accuracy of 84.0%, sensitivity of 78.3%, and specificity of 85.9%. It included four nonophthalmologic factors (sex, age, menopause, and duration of hypertension) and five ophthalmologic factors (IOP, spherical equivalent refractive errors, vertical cup-to-disc ratio, presence of superotemporal retinal nerve fiber layer [RNFL] defect, and presence of inferotemporal RNFL defect).

Conclusions: Though VF tests are considered the most important examination to distinguish OAG from GS, they sometimes are impractical to conduct for small private eye clinics and during large scale medical check-ups. The ANN approach may be a cost-effective screening tool for differentiating OAG patients from GS subjects.