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Dennis L. Barbour

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JBHI Journal 2020 Journal Article

Visual Field Estimation by Probabilistic Classification

Brian Chesley
Dennis L. Barbour

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.

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UAI Conference 2015 Conference Paper

Psychophysical Detection Testing with Bayesian Active Learning

Jacob R. Gardner
Xinyu Song
Kilian Q. Weinberger
Dennis L. Barbour
John P. Cunningham

Psychophysical detection tests are ubiquitous in the study of human sensation and the diagnosis and treatment of virtually all sensory impairments. In many of these settings, the goal is to recover, from a series of binary observations from a human subject, the latent function that describes the discriminability of a sensory stimulus over some relevant domain. The auditory detection test, for example, seeks to understand a subject’s likelihood of hearing sounds as a function of frequency and amplitude. Conventional methods for performing these tests involve testing stimuli on a pre-determined grid. This approach not only samples at very uninformative locations, but also fails to learn critical features of a subject’s latent discriminability function. Here we advance active learning with Gaussian processes to the setting of psychophysical testing. We develop a model that incorporates strong prior knowledge about the class of stimuli, we derive a sensible method for choosing sample points, and we demonstrate how to evaluate this model efficiently. Finally, we develop a novel likelihood that enables testing of multiple stimuli simultaneously. We evaluate our method in both simulated and real auditory detection tests, demonstrating the merit of our approach. 1 Xinyu Song xinyu. song@wustl. edu Washington University in St. Louis St. Louis, MO 63130

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