Visual Field Estimation by Probabilistic Classification

The gold standard clinical tool for evaluating visual dysfunction in cases of glaucoma and other disorders of vision remains the visual field or threshold perimetry exam. Administration of this exam has evolved over the years into a sophisticated, standardized, automated algorithm that relies heavily on specifics of disease processes particular to common retinal disorders. The purpose of this study is to evaluate the utility of a novel general estimator applied to visual field testing. A multidimensional psychometric function estimation tool was applied to visual field estimation. This tool is built on semiparametric probabilistic classification rather than multiple logistic regression. It combines the flexibility of nonparametric estimators and the efficiency of parametric estimators. Simulated visual fields were generated from human patients with a variety of diagnoses, and the errors between simulated ground truth and estimated visual fields were quantified. Error rates of the estimates were low, typically within 2 dB units of ground truth on average. The greatest threshold errors appeared to be confined to the portions of the threshold function with the highest spatial frequencies. This method can accurately estimate a variety of visual field profiles with continuous threshold estimates, potentially using a relatively small number of stimuli.

Authors

• Brian Chesley
• Dennis L. Barbour

Keywords

• Visualization
• Estimation
• Machine learning
• Retina
• Diseases
• Bayes methods
• Probabilistic Classification
• Variation In Profiles
• Semiparametric
• Nonparametric Estimation
• Perimetry
• Psychometric Function
• Error Threshold
• Multidimensional Function
• Root Mean Square Error
• Standard Procedures
• Random Sampling
• Posterior Probability
• Active Learning
• Average Error
• Mean Absolute Error
• Gaussian Process
• Ocular Hypertension
• Covariance Function
• Psychometric Testing
• Latent Function
• Swedish Interactive Threshold Algorithm
• Retinal Location
• Random Stimuli
• Discrete Labels
• Input Domain
• Kernel Images
• Constant Length
• Detection Threshold
• Sigmoid Function
• Active machine learning
• diagnostics
• psychophysics
• threshold perimetry
• visual fields
• Adult
• Aged
• Algorithms
• Humans
• Middle Aged
• Models, Statistical
• Visual Field Tests