Development and validation of a multi-body model of the canine stifle joint

The effect of joint orientation on passive movement of the dog's stifle

Introduction

The cranial cruciate ligament (CCL) is one of numerous structures which determine the path of the tibia relative to the femur when passively flexing/extending the stifle of the dog. The effect of cutting the CCL on passive motion with the hind limb in different orientations, is unknown. The aim of this study was to describe passive movement of the tibia relative to the femur in dogs, with the hind limb in three different orientations, and with CCL intact and cut.

Methods

Ten cadaveric hind limbs were obtained from dogs weighing between 20 kg and 25 kg and prepared for testing in a custom-built joint testing machine. Each hind limb was tested in three different orientations with data collected, using an electromagnetic tracking system, during 2 cycles of flexion/extension with the CCL intact and cut. Each cycle was initiated with the stifle in full extension (0°) and data was collected at 0°, 20°, 30°, 40°, 45°, and 55° of stifle flexion/extension.

Results

Flexion of the stifle resulted in caudal translation and internal rotation of the tibia relative to the femur, with cranial translation and external rotation occurring during extension along the identical path. Cutting the cranial cruciate ligament did not result in significant differences in translation or rotation when the stifle was orientated to approximated the standing position of a dog.

Discussion

Isometric points at the origin and insertion of the CCL can potentially be identified in CCL deficient stifles using a technique based on passive motion of an intact stifle.

Development of a three-dimensional computer model of the canine pelvic limb including cruciate ligaments to simulate movement

Gait analysis as subjective visual assessment forms the foundation of the veterinarian's lameness examination. Pelvic limb lameness is frequently seen in dogs and the stifle joint with its cruciate ligaments, is a main cause of lameness due to cruciate ligament deficiency. In this study, we developed an open-source three-dimensional musculoskeletal pelvic limb model of a 30 kg Labrador Retriever including cruciate ligaments, simulating the gait cycle of the walking movement with the open-source programs NMSBuilder (Institutio Ortopedico Rizzoli, Bologna, Italy) and OpenSim (National Center for Simulation in Rehabilitation Research (NCSRR), Stanford, CA, USA). The computer model generated muscle activations based on motion data. The computed activations were similar to experimental electromyogram data. Highest joint torque was in extension/flexion in the stifle joint at 54 Nm at 14% of the gait cycle with cruciate ligaments. Highest stifle joint reaction force was 408 N at 16% of the gait cycle and was reduced after adding cruciate ligaments. Especially the cranial cruciate ligament loads up to 102 N (34% body weight). Cranial cruciate ligament forces increase with stifle extension and decrease with stifle flexion. On the contrary, the caudal cruciate ligament loads up to 27 N (9% body weight) during the swing phase with a flexed stifle joint. The model was validated with electromyogram data. The model's predictions are plausible because joint torques and forces match the applied ground reaction forces in curve progression and in timing. This model forms a basis for further investigations into stifle surgery after cruciate ligament deficiency.

Three-dimensional motion of the patella in French bulldogs with and without medial patellar luxation

Background

French bulldogs exhibit significantly larger femoral external rotation and abduction than other breeds. We were curious as to whether this peculiar leg kinematic affects patellar motion and/or might induce medial patellar subluxation (MPSL) or medial patellar permanent luxation (MPPL). We hypothesized that the more abducted leg posture during stance causes an unusual medial pull direction of the rectus femoris muscle during stance, and that this may facilitate the occurrence of MPSL or even MPPL during locomotion. To test our hypothesis, we analyzed existing stifle-joint X-ray-sequences collected during the treadmill walk and trot of seven adult female French bulldogs. We estimated 3D-patellar kinematics using Scientific Rotoscoping.

Results

The three-dimensional motion of the patella comprises rotations and translations. From the seven dogs analyzed, three exhibited MPSL and one MPPL during the gait cycle. Medial patellar luxation (MPL) occurred mostly around toe-off in both gaits studied.

Patellar position was generally not gait-related at the analyzed timepoints. In dogs with MPL, the patella was placed significantly more distally (p = 0.037) at touch-down (TD) and at midswing (p = 0.024), and significantly more medial at midswing (p = 0.045) compared to dogs without MPL.

Conclusions

Medial patellar luxation seems to be the consequence of the far from parasagittal position of the stifle joint during stance due to a broad trunk, and a wide pelvis. This peculiar leg orientation leads to a medial sideway pull caused by the rectus femoris muscle and the quadriceps femoris and may initiate plastic deformation of the growing femur and tibia. Thus, a way to avoid MPL could be to control breeding by selecting dogs with lean bodies and narrow pelvis. Actual breeding control programs based on the orthopedic examination are susceptible to errors. Systematic errors arise from the fact that the grading system is highly dependent on the dog’s condition and the veterinarians’ ability to perform the palpation on the stifle. Based on our results, the position of the patella at TD, or even perhaps during stand might offer a possibility of an objective radioscopic diagnostic of the MPL.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-02787-z.

Medial Collateral Ligament Deficiency of the Elbow Joint: A Computational Approach

Computational elbow joint models, capable of simulating medial collateral ligament deficiency, can be extremely valuable tools for surgical planning and refinement of therapeutic strategies. The objective of this study was to investigate the effects of varying levels of medial collateral ligament deficiency on elbow joint stability using subject-specific computational models. Two elbow joint models were placed at the pronated forearm position and passively flexed by applying a vertical downward motion on humeral head. The models included three-dimensional bone geometries, multiple ligament bundles wrapped around the joint, and the discretized cartilage representation. Four different ligament conditions were simulated: All intact ligaments, isolated medial collateral ligament (MCL) anterior bundle deficiency, isolated MCL posterior bundle deficiency, and complete MCL deficiency. Minimal kinematic differences were observed for isolated anterior and posterior bundle deficient elbows. However, sectioning the entire MCL resulted in significant kinematic differences and induced substantial elbow instability. Joint contact areas were nearly similar for the intact and isolated posterior bundle deficiency. Minor differences were observed for the isolated anterior bundle deficiency, and major differences were observed for the entire MCL deficiency. Complete elbow dislocations were not observed for any ligament deficiency level. As expected, during isolated anterior bundle deficiency, the remaining posterior bundle experiences higher load and vice versa. Overall, the results indicate that either MCL anterior or posterior bundle can provide anterior elbow stability, but the anterior bundle has a somewhat bigger influence on joint kinematics and contact characteristics than posterior one. A study with a larger sample size could help to strengthen the conclusion and statistical significant.

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

A dynamic musculoskeletal model of the elbow joint in which muscle, ligament, and articular surface contact forces are predicted concurrently would be an ideal tool for patient-specific preoperative planning, computer-aided surgery, and rehabilitation. Existing musculoskeletal elbow joint models have limited clinical applicability because of idealizing the elbow as a mechanical hinge joint or ignoring important soft tissue (e.g., cartilage) contributions. The purpose of this study was to develop a subject-specific anatomically correct musculoskeletal elbow joint model and evaluate it based on experimental kinematics and muscle electromyography measurements. The model included three-dimensional bone geometries, a joint constrained by multiple ligament bundles, deformable contacts, and the natural oblique wrapping of ligaments. The musculoskeletal model predicted the bone kinematics reasonably accurately in three different velocity conditions. The model predicted timing and number of muscle excitations, and the normalized muscle forces were also in agreement with the experiment. The model was able to predict important in vivo parameters that are not possible to measure experimentally, such as muscle and ligament forces, and cartilage contact pressure. In addition, the developed musculoskeletal model was computationally efficient for body-level dynamic simulation. The maximum computation time was less than 30 min for our 35 s simulation. As a predictive clinical tool, the potential medical applications for this model and modeling approach are significant.

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Traditional finite element (FE) analysis is computationally demanding. The computational time becomes prohibitively long when multiple loading and boundary conditions need to be considered such as in musculoskeletal movement simulations involving multiple joints and muscles. Presented in this study is an innovative approach that takes advantage of the computational efficiency of both the dynamic multibody (MB) method and neural network (NN) analysis. A NN model that captures the behavior of musculoskeletal tissue subjected to known loading situations is built, trained, and validated based on both MB and FE simulation data. It is found that nonlinear, dynamic NNs yield better predictions over their linear, static counterparts. The developed NN model is then capable of predicting stress values at regions of interest within the musculoskeletal system in only a fraction of the time required by FE simulation.