Computational modeling is very useful in biomechanics to simulate normal and pathologic joint function. It is also useful to determine the efficacies of various surgical procedures performed to treat joint pathologies and simulate their outcomes. Models can be used to estimate in situ measures such as contact pressure distributions that are difficult to acquire through experiments noninvasively. Currently, computational modeling is the only technique available to noninvasively evaluate in vivo joint contact mechanics [1]. However, most models make use of input parameters derived from various general sources such as literature, standards, or experiments and are, therefore, limited for patient-specific applications [2]. Joint injuries, whether ligaments or articular surface, are a significant problem and there is still a need for tools to effectively evaluate joint injuries and associated sequelae [3]. The ability to monitor the initiation and progression of joint instability after injury may aid in determining prognosis, leading to better treatment algorithms. In order to refine or develop treatments that are targeted toward individuals, it is important to focus on subject-specific models. Several modeling techniques exist to evaluate in vivo joint mechanics. The common techniques include image-based FEM [4–13], rigid body spring modeling/discrete element analysis (RBSM) [14–16], or SCM [17–19]. The models are either displacement driven or force driven. Generally, model geometries are acquired from modalities such as computed tomography (CT) [4–8,14,15,19] or MRI [9–11,17,18]. Kinematics are determined through external (surface markers) or internal (biplanar radiography) measures, while tendon forces are estimated from corresponding musculature electromyography (EMG) and cross-sectional area, and ground reaction forces are measured using force platforms [11,20]. These loads and displacement boundary conditions are input into the model to infer joint kinetics/kinematics and resulting surface and/or volumetric stresses and strains. FEM is the most common and accurate method to determine stresses [11,20,21]. However, depending on the complexity of the problem, the process of developing the mesh can be laborious and obtaining a converged solution can be computationally intensive [22], which limits its clinical applicability. Depending on the type of problem (for instance, deformable versus rigid), more simplified analyses can be performed based on relevant assumptions to determine appropriate solutions. This is the basis of RBSM and SCM techniques. Using these methods, joint mechanics can be evaluated in a computationally efficient manner compared to FEM [23], which makes them relevant for clinical applications. The underlying question is whether these methods are competent to provide data that are sufficiently accurate for the intended application. The ability to accurately determine joint mechanics has wide clinical implications, especially in complex joints such as the wrist. It may be possible to sufficiently evaluate changes in joint mechanics as a result of injury or surgical intervention from surface contact mechanics data alone. This can be achieved through the SCM technique, without the need for a complex volumetric analysis [24]. However, the SCM technique has not been extensively used for orthopedic applications. Computational modeling has been extensively applied to the lower extremity to evaluate in vivo joint mechanics [4–11,18]. In the wrist, studies have evaluated in vivo joint mechanics during functional activities [12,14,16,17] and have also simulated the effects of some carpal fractures and limited fusions [13,15,19]. Scapholunate (SL) ligament injury is a commonly occurring wrist ligament injury that can lead to SL joint instability and progressive degenerative changes [25–28]. Prior modeling work on the in vivo effects of SL ligament injury or surgical repair appears to be limited [29,30]. Hence, we investigated differences in radiocarpal in vivo joint mechanics obtained from SCM for normal wrists, after SL ligament injury, and after surgical repair to results from the FEM “gold standard.” We did not intend to make comparisons between the normal, injured, and postoperative states. Our goal was to show that contact outcomes obtained from SCM would be comparable to those obtained from a similar FEM analysis regardless of wrist state and to demonstrate the feasibility and applicability of the SCM technique.