Computational reverse shoulder prosthesis model: Experimental data and verification

Complications of Reverse Total Shoulder Arthroplasty: A Computational Modelling Perspective

“…For example, shoulder muscle forces during upper limb elevation can be calculated in several minutes using a subject-specific rigid body model (Figure 1A) [28,31]; however, since deformation mechanics is neglected, the internal stresses and strains in the bone and implant cannot be accurately evaluated using this method. An important utility of rigid body models has been in their capacity to estimate the influence of arthroplasty on muscle moment arms, muscle and joint forces, and range of motion (ROM) at the shoulder, which are primary indicators of implant functional performance [21,23,24,[29][30][31][32][33][34][35][36][37][38][39][40].…”

“…Computational models of the reverse shoulder that are used to predict shoulder ROM can be validated against experimental measurements of kinematics [84]; muscle moment arms compared against in vitro measurements, which are commonly obtained using the tendon excursion method [4,5]; muscle line of action data validated using experimental measurements [85]; muscle recruitment and timing of muscle activities validated against EMG recordings on patients [36]; and joint force predictions validated using cadaveric simulations [85].…”

“…While computational modeling may provide a basis for estimates of muscle and joint function, even the most sophisticated anatomical models are limited by the degree to which they replicate bone and joint biomechanics in vivo. Virtually all rigid body models neglect glenohumeral joint translation, and represent the glenohumeral joint as a threedegree-of-freedom constrained ball and socket or spherical joint [21,24,30,[32][33][34][35][36][37][38]40,93]. This simplification limits the degree to which such a model can predict joint stability, since joint subluxation cannot be explicitly simulated.…”