Predicting nerve guidance conduit performance for peripheral nerve regeneration using bootstrap aggregated neural networks

The inability to identify the optimal construction of a nerve guidance conduit (NGC) for peripheral nerve regeneration is a challenge in the field of tissue engineering. This is attributed to the vast number of parameters that can be combined in varying quantities. A pre-existing normalization standard is applied in this paper which uses a calculated ratio of gap length divided by the graft's critical axon elongation denoted as L/Lc. This allows for a direct comparison of the nerve regenerative activity, a measure of performance, of any NGC across an array of gap lengths relative to a standard nerve conduit. Data was extracted from a total of 28 scientific publications that compared the nerve regenerative activity of experimental NGCs relative to standard NGCs. Of the extracted data, 40 parameters were identified that impacted the performance of the experimental conduits. We demonstrate how bootstrap aggregated neural networks provides substantial increases in accuracy in predicting the performance of a NGC over a single neural network and previous prediction attempts by the SWarm Intelligence based Reinforcement Learning (SWIRL) system. The improved accuracy will provide for a better understanding and insight for theorizing successful strategies for NGC development.