  The response of adult human bone marrow stromal stem cells to surface
topographies generated through femtosecond laser machining can be predicted by
a deep neural network. The network is capable of predicting cell response to a
statistically significant level, including positioning predictions with a
probability P < 0.001, and therefore can be used as a model to determine the
minimum line separation required for cell alignment, with implications for
tissue structure development and tissue engineering. The application of a deep
neural network, as a model, reduces the amount of experimental cell culture
required to develop an enhanced understanding of cell behavior to topographical
cues and, critically, provides rapid prediction of the effects of novel surface
structures on tissue fabrication and cell signaling.
