  Mechanical properties of tissue provide valuable information for identifying
lesions. One approach to obtain quantitative estimates of elastic properties is
shear wave elastography with optical coherence elastography (OCE). However,
given the shear wave velocity, it is still difficult to estimate elastic
properties. Hence, we propose deep learning to directly predict elastic tissue
properties from OCE data. We acquire 2D images with a frame rate of 30 kHz and
use convolutional neural networks to predict gelatin concentration, which we
use as a surrogate for tissue elasticity. We compare our deep learning approach
to predictions from conventional regression models, using the shear wave
velocity as a feature. Mean absolut prediction errors for the conventional
approaches range from 1.32$\pm$0.98 p.p. to 1.57$\pm$1.30 p.p. whereas we
report an error of 0.90$\pm$0.84 p.p for the convolutional neural network with
3D spatio-temporal input. Our results indicate that deep learning on
spatio-temporal data outperforms elastography based on explicit shear wave
velocity estimation.
