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Using Machine Learning Classifiers to Identify Glaucomatous Change Earlier in Standard Visual Fields

¹ From the Glaucoma Center, Department of Ophthalmology, and the ² Institute for Neural Computation, University of California at San Diego, La Jolla, California; the ⁴ Computational Neurobiology Laboratory, Salk Institute, La Jolla, California; the ³ Department of Ophthalmology, University of Dresden, Germany; and ⁵ Discoveries in Sight, Devers Eye Institute, Portland, Oregon.

PURPOSE. To compare the ability of several machine learning classifiers to predict development of abnormal fields at follow-up in ocular hypertensive (OHT) eyes that had normal visual fields in baseline examination.

METHODS. The visual fields of 114 eyes of 114 patients with OHT with four or more visual field tests with standard automated perimetry over three or more years and for whom stereophotographs were available were assessed. The mean (±SD) number of visual field tests was 7.89 ± 3.04. The mean number of years covered (±SD) was 5.92 ± 2.34 (range, 2.81–11.77). Fields were classified as normal or abnormal based on Statpac-like methods (Humphrey Instruments, Dublin, CA) and by several machine learning classifiers. The machine learning classifiers were two types of support vector machine (SVM), a mixture of Gaussian (MoG) classifier, a constrained MoG, and a mixture of generalized Gaussian (MGG). Specificity was set to 96% for all classifiers, using data from 94 normal eyes evaluated longitudinally. Specificity cutoffs required confirmation of abnormality.

RESULTS. Thirty-two percent (36/114) of the eyes converted to abnormal fields during follow-up based on the Statpac-like methods. All 36 were identified by at least one machine classifier. In nearly all cases, the machine learning classifiers predicted the confirmed abnormality, on average, 3.92 ± 0.55 years earlier than traditional Statpac-like methods.

CONCLUSIONS. Machine learning classifiers can learn complex patterns and trends in data and adapt to create a decision surface without the constraints imposed by statistical classifiers. This adaptation allowed the machine learning classifiers to identify abnormality in visual field converts much earlier than the traditional methods.

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