Prediction of peripheral nerve intrinsic organisation: a pilot study

Abstract

Traumatic peripheral nerve injuries (PNI) are debilitating and can leave patients severely limited with drastic changes in quality of life. PNI treatment options are varied; however, only 50% of those patients regain any useful function. The complex intrinsic organisation of nerves is largely contribute to this issue. This research aimed at creating a reliable way of isolating and predicting the location of peripheral nerve fascicles on microscopic images of nerve cross-sections via semantic segmentation and machine learning. Using the Anaconda Python platform, a machine learning algorithm was programmed using a random forest classifier. Training data resulted in a 91% accuracy rate with respect to correctly identifying image pixels as fascicle or not. When the trained model was tested in the real world with unseen data, the results varied. However, the model retained the ability to identify fascicles and isolate them from background. The development of clinically successful PNI treatments has long been hindered by intrinsic nerve structures that machine learning may be able to solve. With further development, this research has the potential to expand and form new frontiers in the field of clinical medicine and drastically improve outcomes of PNI.

Keywords

• Machine learning
• semantic segmentation
• peripheral nerve injury
• fascicles
• nerves