A machine learning approach for Colles' fracture treatment diagnosis

Abstract

Wrist fractures (e.g. Colles fracture) are the most common injuries in the upper extremity treated in Emergency Departments. Most patients are treated with a procedure called Manipulation under Anaesthesia. Surgical treatment may still be needed in complex fractures or if the wrist stability is not restored. This can lead to inefficiency in constrained medical resources and patients inconvenience. Previous geometric measurements in X-ray images were found to provide statistical differences between healthy controls and fractured cases as well as pre- and post-intervention images. The most discriminating measurements were associated with the texture analysis of the radial bone. This work presents further analysis of these measurements and applying them as features to identify the best machine learning model for Colles fracture treatment diagnosis. Random forest was evaluated to be the best model based on validation accuracy. The non-linearity of the measurement features has attributed to the superior performance of an ensembled tree-based model. It is also interesting that the most important features (i.e. texture and swelling) required in the optimised random forest model are consistent with previous findings.