Purpose: To determine the agreement of six established visual field progression algorithms in a large dataset of visual fields from multiple institutions and to determine predictors of discordance amongst these algorithms. Design: Retrospective Longitudinal CohortSubjects, Participants, and/or Controls: Visual fields from five major eye care institutions in the United States. This analysis included a subset of eyes with at least five SITA-Standard 24-2 visual fields that met our reliability criteria. Of a total of 831,240 fields, a subset of 90,713 visual fields of 13,156 eyes of 8,499 patients met the inclusion criteria.

To develop and test machine learning classifiers (MLCs) for determining visual field progression.Methods: In total, 90,713 visual fields from 13,156 eyes were included. Six different progression algorithms (linear regression of mean deviation, linear regression of the visual field index, Advanced Glaucoma Intervention Study algorithm, Collaborative Initial Glaucoma Treatment Study algorithm, pointwise linear regression [PLR], and permutation of PLR) were applied to classify each eye as progressing or stable. Six MLCs were applied (logistic regression, random forest, extreme gradient boosting, support vector classifier, convolutional neural network, fully connected neural network) using a training and testing set. For MLC input, visual fields for a given eye were divided into the first and second half and each location averaged over time within each half. Each algorithm was tested for accuracy, sensitivity, positive predictive value, and class bias with a subset of visual fields labeled by a panel of three experts from 161 eyes.Results: MLCs had similar performance metrics as some of the conventional algorithms and ranged from 87% to 91% accurate with sensitivity ranging from 0.83 to 0.88 and specificity from 0.92 to 0.96. All conventional algorithms showed significant class bias, meaning each individual algorithm was more likely to grade uncertain cases as either progressing or stable (P ≤ 0.01). Conversely, all MLCs were balanced, meaning they were equally likely to grade uncertain cases as either progressing or stable (P ≥ 0.08).Conclusions: MLCs showed a moderate to high level of accuracy, sensitivity, and specificity and were more balanced than conventional algorithms.Translational Relevance: MLCs may help to determine visual field progression.

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