In silico model for automated calculation of functional metrics in animal models of peripheral nerve injury repair.

The rat sciatic nerve model is commonly used to test novel therapies for nerve injury repair. The static sciatic index (SSI) is a useful metric for quantifying functional recovery, and involves comparing an operated paw versus a control paw using a weighted ratio between the toe spread and the internal toe spread. To calculate it, rats are placed in a transparent box, photos are taken from underneath and the toe distances measured manually. This is labour intensive and subject to human error due to the challenge of consistently taking photos, identifying digits and making manual measurements. Although several commercial kits have been developed to address this challenge, they have seen little dissemination due to cost. Here we develop a novel algorithm for automatic measurement of SSI metrics based on video data using casted U-Nets. The algorithm consists of three U-Nets, one to segment the hind paws and two for the two pairs of digits which input into the SSI calculation. A training intersection over union error of 60 % and 80 % was achieved for the back paws and for both digit segmentation U-Nets, respectfully. The algorithm was tested against video data from three separate experiments. Compared to manual measurements, the algorithm provides the same profile of recovery for every experiment but with a tighter standard deviation in the SSI measure. Through the open-source release of this algorithm, we aim to provide an inexpensive tool to more reliably quantify functional recovery metrics to the nerve repair research community.