Fig 1.
Architectures of the bidirectional gated recurrent unit (Bi-GRU) model.
Input data comprised 3 categories: relative time displacement in days, reliability data, and visual field (VF) data. To accommodate a variable number of input VF exams, a special layer called a masking layer was used, which monitors the input data and recognizes an empty vector with all values set to “0”. This allows the Bi-GRU model to accept a variety of input VF exams up to 80 VF exams by ignoring empty vectors. Reliability data comprised false positive rate (FP), false negative rate (FN), and fixation loss rate (FL). VF data comprised mean deviation (MD), pattern standard deviation (PSD), Visual Field Index (VFI), 54 pattern deviation values (PDVs), and 54 total deviation values (TDVs) of 24–2 standard automated perimetry (including 2 points of physiologic scotoma).
Table 1.
Demographics of patients in the training dataset.
Table 2.
Demographics of patients in the test dataset.
Table 3.
Prediction errors according to glaucoma severity.
Table 4.
Prediction error according to number of input visual field exams.
Table 5.
Prediction error according to prediction interval.
Fig 2.
Prediction errors of mean deviation (MD) (A), pattern standard deviation (PSD) (B), and Visual Field Index (VFI) (C) were binned according to number of visual field input exams and prediction time interval.
Fig 3.
Prediction errors of total deviation values (TDV) mean absolute error (MAE) (A) and root mean squared error (RMSE) (B) binned according to the number of visual field input exams and prediction time interval.