Aim: To investigate the usefulness of data augmentation in visual field (VF) trend analyses in patients with glaucoma.
Method: This study included 6380 VFs from 638 eyes of 417 patients with open-angle glaucoma. Various affine transformations were applied to augment the VF data: (1) rotation, (2) scaling, (3) vertical and horizontal shift and (4) a combination of these different transformations. Using pointwise linear regression (PLR), the total deviation (TD) values of a patient's 10th VF were predicted using TD values from shorter VF series (from first to third VFs (VF1-3) to first to ninth VFs (VF1-9)) with and without VF data augmentation, and the root mean squared error (RMSE) was calculated.
Results: With PLR, mean RMSE without VF augmentation averaged from 3.95 (VF1-3) to 19.01 (VF1-9) dB. The RMSE was significantly improved by applying the different transformations: (1) rotation (from VF1-3 to VF1-7), (2) scaling (from VF1-3 to VF1-6), (3) vertical and horizontal shifts (from VF1-3 to VF1-4) and (iv) a combination of these (from VF1-3 to VF1-7). Progression rates in VF1-10 had better agreement with those in shorter VF series when a combination of affine transformation was applied. The differences in rates were between 1.9 (VF1-3) and 0.39 (VF1-9) dB if augmentation was used, which was significantly smaller than that observed when augmentation was not applied (from 2.6 with VF1-3 to 0.26 dB with VF1-9).
Conclusion: It is useful to apply VF data augmentation techniques when predicting future VF progression in glaucoma using PLR, especially with short VF series.
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