Modelling the form and function of the trapeziometacarpal joint

Abstract

The trapeziometacarpal (TMC) joint is involved in over 80 % of activities of daily living. However, the joint is susceptible to degradation and osteoarthritic degeneration which can impair the upper extremity by up to 50 %, profoundly affecting the quality of life. The aetiology of TMC osteoarthritis is poorly understood, but biomechanical factors are implicated at its pathogenesis. The aim of this work was to create a computational framework to investigate the relationship between TMC morphology, kinematics, and cartilage stress distributions in men and women with and without degenerative disease. We developed a statistical shape model of the TMC joint, an automatic 3D segmentation tool, a parametric population finite element model, and a shape model classifier. These tools can be combined to create an automatic pipeline for predicting cartilage stress and strain from CT data. The framework was used to: characterise the morphology in healthy TMC joints as a function of sex and age, classify cartilage stress distributions across three tasks as a function of sex and age, and characterise the morphological differences between healthy and early osteoarthritic TMC joints. Our findings showed that there is little variation in morphology of the TMC joint with sex, but suggested that size may play a role in osteoarthritis. From our finite element model, we found that women exhibited higher variation in joint contact, suggesting greater variability in neuromuscular control of thumb joint kinematics. Furthermore, we found a subset of men and women that exhibited peak cartilage stresses in regions with frequent cartilage wear, suggesting that cartilage degeneration may occur through divergent mechanisms. Our classifier showed that early morphologic changes were present at the volar beak of the first metacarpal and throughout the trapezial articular surface in early osteoarthritic joints, and showed promise for a future diagnostic tool. These methods can be combined to create a pipeline that predicts cartilage stress and strain from CT images. These findings contribute towards understanding the normal biomechanics of the TMC joint and the aetiology of TMC osteoarthritis.