IN VIVO CONTACT MECHANICS OF THE DISTAL RADIOULNAR JOINT WITH AND WITHOUT SCAPHOLUNATE DISSOCIATION

The distal radioulnar joint (DRUJ) is a joint of the wrist which allows force transmission and forearm rotation in the upper limb while preserving the stability of the forearm independent of elbow and wrist flexion and extension. DRUJ is a commonly injured part of the body. Conditions affecting the joint could be positive ulnar variance or negative ulnar variance, the length of the ulna relative to radius. It is also adversely affected by nearby injuries such as distal radial fractures. In fact, a significant correlation was found between negative ulnar variance and scapholunate dissociation (SLD), a ligament injury of the wrist. This leads to the question of whether or not SLD causes changes in the radioulnar joint mechanics. Altered joint mechanics are associated with the onset of osteoarthritis (OA). An understanding of the of the normal and pathological wrist in vivo DRUJ contact mechanics should help physicians make better clinical recommendations and improve treatment for the primary injury. Proper treatment of the DRUJ could help prevent the onset of OA. Image registration is used in our modeling to determine the kinematic transformations for carpal bones from the unloaded to the loaded configuration. A perturbation study was done to evaluate the effect of varying initial manual registrations and the relative image plane orientations on the final registration kinematics. The results of the study showed that Subject II (with different imaging plane orientations) was found to have greater translation errors compared to subject I (consistent imaging planes). This result emphasizes the need to be consistent with forearm position and/or image plane orientation to minimize the errors of translation and attitude vectors. In a separate study, five additional subjects with unilateral SLD participated in another study in which MRI based contact modeling was used to analyze the contact mechanics parameters of the injured wrist compared to the normal wrist. The contact forces, peak contact pressures, average pressures and contact areas generally trended to be higher in injured wrists compared to the normal and surgically repaired wrists. Model contact areas were found to be consistent with the directly measured areas from the grasp MR images. A repeatability test was done on a single subject and the absolute differences between the contact parameters for both the trials were close. These findings suggest that SLD injury of the wrist may have an effect on the DRUJ mechanics

Predicting the Progression of Diabetes Mellitus Using Dynamic Plantar Pressure Parameters

Introduction: Diabetic peripheral neuropathy is one of the common complications of type-2 diabetes mellitus (DM). Changes in the intrinsic plantar tissue coupled with repetitive mechanical loads and loss of sensation may lead to foot related complications (skin break down, ulcerations, and amputations) in persons with neuropathy if left untreated. The purpose of this dissertation was to stratify individuals with pre-diabetes, diabetes with and without neuropathy using dynamic plantar pressure parameters during walking, using machine learning algorithms.Methods: Plantar pressure data was collected from one hundred participants during walking with pressure measuring insoles fixed between the feet and thin socks. Simultaneously high-definition videos were collected using a camera placed behind the participants. Walking speed was computed in the narrow walkways established in laboratory and clinical settings using a single calibrated camera. Support vector machine algorithms were implemented using dynamic plantar pressure parameters and participant-specific parameters to predict group classification. Results: The camera calibration approach estimated the walking speed at three different physical locations with good to excellent intra-rater reliability. No significant differences in plantar pressure measures were found among the three participant groups using traditional statistical analysis. Support vector machine classifiers were able to successfully classify the participant groups with very high sensitivity and specificity. Conclusions: The study’s findings may allow early detection of neuropathy in persons with DM using quantitative measures of the dynamic plantar pressure distribution, which may prevent foot ulcerations. Future implementation of machine learning algorithms will offer a data-driven approach for clinicians to provide better prevention and treatment strategies to persons with DM to avoid foot related complications and, improve health and quality of life