Automatic detection of glaucomatous visual field progression with neural networks

Objective: To evaluate computerized neural networks to determine visual field progression in patients with glaucoma. Methods: Two hundred thirty-three series of Octopus G1 visual fields of 181 patients with glaucoma were collected. Each series was composed of 4 or more reliable visual fields from patients who had previously undergone automated perimetry. The visual fields were independently evaluated in a masked fashion by 3 experienced observers (K.N.-M, M.W., and J.C.) and were judged to show progression based on the agreement of 2 observers. The stable and progressed series were matched for mean defect at baseline. The threshold data were submitted to a back propagation neural network that was trained to classify each series as stable or progressed. Two thirds of the data were used for the training and the remaining one third to test the performance of the network. This was repeated 3 times to classify all of the series (changing the training and test series). Results: Fifty-nine series of visual fields showed progression and 151 were judged stable. Neural network sensitivity was 73% and specificity was 88% (threshold for progression = 0.5). The concordance of the neural network with the observers was good (0.50 less than or equal to kappa greater than or equal to 0.64). Conclusions: A neural network can be trained to recognize visual field progression in good concordance with experienced observers. Neural networks may be used to aid the physician in the evaluation of glaucomatous visual field progression.