Neural networks to identify glaucomatous visual field progression.
OBJECTIVE
To describe a method to determine progression of glaucoma based on visual field thresholds.


METHODS
Observational retrospective longitudinal cohort study.


METHODS
A back propagation neural network with three hidden layers was developed with commercial software. Visual field data from 80 patients who participated in the Advanced Glaucoma Intervention Study (AGIS) were used. Glaucomatous visual field progression was defined as a change of 4 or more units in the AGIS score, confirmed by at least two sequential subsequent tests. Inputs to the neural network consisted of threshold measurements from 55 visual field locations from the baseline examination and each follow-up examination. The data set was randomized so the sequence of examinations would not influence the training or testing of the neural network. Two thirds of the randomized data were used for training and the remaining one third for testing.


RESULTS
The mean age of 80 patients enrolled in AGIS at initial examination was 67.4 (+/- 7.3 standard deviation [SD]) years. The average follow-up period was 7.2 (+/-2.3 SD) years and the mean duration between examinations was 0.46 (+/- 0.39 SD) years. The neural network estimated the probability of progression for each baseline and follow-up comparison with an average sensitivity of 86% and specificity of 88%. The area under the receiver operating characteristic (ROC) curve was 0.92, with a sensitivity of 86% at the 80% specificity level and a sensitivity of 91% at the 90% specificity level.


CONCLUSIONS
From analysis of AGIS data, progression of glaucoma could be detected from visual field thresholds with a neural network.
Lin
Amy
A
Northwestern University, Chicago, Illinois, USA. aviendha@northwestern.edu
Hoffman
Douglas
D
Gaasterland
Douglas E
DE
Caprioli
Joseph
J
D000368:Aged; D015331:Cohort Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D018450:Disease Progression; D005189:False Positive Reactions; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D007429:Intraocular Pressure; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011336:Probability; D012372:ROC Curve; D011897:Random Allocation; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014786:Vision Disorders / Q000175:diagnosis*; D014794:Visual Fields