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. Treatment for most patients is an intervention called Manipulation under Anaesthesia (MUA). Surgical treatment would be needed for complex fractures or if the wrist stability is not restored. In addition, an unsuccessful treatment via MUA may also require subsequent surgical operation causing inefficiency in constrained medical resources and patients’ inconvenience. Previous geometric measurements in X-ray images [21] were found to provide statistical differences between healthy controls and patients with fractures, 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 an appropriate machine learning model for Colles’ fracture treatment diagnosis. Random forest was evaluated to be the best model based on classification accuracy among the selected models commonly used in similar research. 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 [21].