  Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted
regularly to monitor disease progression. Here we address the question to what
degree aggregate VFT measurements such as Visual Field Index (VFI) and Mean
Deviation (MD) can be inferred from Optical Coherence Tomography (OCT) scans of
the Optic Nerve Head (ONH) or the macula. Accurate inference of VFT
measurements from OCT could reduce examination time and cost. We propose a
novel 3D Convolutional Neural Network (CNN) for this task and compare its
accuracy with classical machine learning (ML) algorithms trained on common,
segmentation-based OCT, features employed for glaucoma diagnostics. Peak
accuracies were achieved on ONH scans when inferring VFI with a Pearson
Correlation (PC) of 0.88$\pm$0.035 for the CNN and a significantly lower (p $<$
0.01) PC of 0.74$\pm$0.090 for the best performing, classical ML algorithm - a
Random Forest regressor. Estimation of MD was equally accurate with a PC of
0.88$\pm$0.023 on ONH scans for the CNN.
