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.