Modeling adult skeletal stem cell response to laser-machined topographies through deep learning

TL;DR

This paper demonstrates that a deep neural network can accurately predict adult skeletal stem cell responses to laser-machined surface topographies, aiding tissue engineering by reducing experimental efforts and enabling rapid assessment of new designs.

Contribution

It introduces a deep learning model that predicts stem cell responses to surface topographies, significantly advancing tissue engineering research and design efficiency.

Findings

Neural network predicts cell response with P < 0.001

Model determines minimum line separation for cell alignment

Reduces experimental cell culture requirements

Abstract

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.