Lateral collateral ligament deficiency of the elbow joint: A modeling approach

Rahman, Munsur; Çil, Akın; Bogener, James; Stylianou, Antonis P.

doi:10.1002/jor.23165

Cited by 10 publications

(10 citation statements)

References 48 publications

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“…The ligaments and the interosseous membranes were modelled as a different number of bundles based on their structure and function. The model included three bundles for the medial collateral ligament (MCL) anterior part [ 14 ], three bundles for the MCL posterior part, three bundles for the lateral ulnar collateral ligament (LUCL) [ 15 ], three bundles for the radial collateral ligament (RCL) [ 8 ], and two bundles for the annular ligament ( Figure 2 b,c). The ligaments were attached to the bone, according to the attachment sites identified in the MRI and in published studies [ 1 , 8 , 16 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

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Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

et al. 2018

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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.

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Section: Methodsmentioning

confidence: 99%

“…A custom macro was written in ADAMS to automatically divide the humerus cartilage into discrete hexahedral elements. The macro performance was successfully tested in many of our previous lab studies [ 15 , 24 , 25 , 26 , 27 , 28 , 29 ]. Each cartilage element had an approximate 3 × 3 mm cross-sectional area.…”

Section: Methodsmentioning

confidence: 99%

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

et al. 2018

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“…Since the humerus cartilage was discretized in 5 × 5 mm, the value of p / d was then multiplied by 25 mm 2 to estimate contact stiffness. The final values used in Equations (1) and (2) are: kc = 126 N/mm (ulnohumeral) & 105 N/mm (radiohumeral), N = 1, Bmax = 2 Ns/mm, and dmax = 0.1 [13]. Based on our previous experience, these values of n, Bmax, and dmax work well for the dynamic simulation without any significant alteration effect on joint kinematics and contact characteristics.…”

Section: Methodsmentioning

confidence: 99%

“…Such models would allow us to examine efficient ligament reconstruction techniques by pre-operative assessment and to investigate better rehabilitation post-operative protocols. Literature reviews reveal that computer models have been employed effectively to measure articular cartilage contact pressure distribution, examine muscle and ligament function, investigate joint stability, and injury mechanisms [6,7,8,9,10,11,12,13,14,15,16,17,18]. Computer models provide great flexibility in analyzing different clinical scenarios and are capable of measuring and calculating important parameters that are difficult or sometimes impossible to capture experimentally such as ligament force and cartilage contact pressure.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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“…[23][24][25][26]30 Spratley and Wayne 24 simulated injury models to create the varus instability of the forearm. Rahman et al 26 examined the effects of ligament sectioning on elbow joint characteristics. While finite element (FE) analysis is necessary when quantifying the stress/strain in the structures as well as investigating the interaction between the musculoskeletal models with deformable bodies.…”

Section: Introductionmentioning

confidence: 99%

Contribution of a distal radioulnar joint stabilizer on forearm stability: A modeling study

Khuyagbaatar

Lee²,

Bayarjargal

et al. 2021

Proc Inst Mech Eng H

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Instability of the forearm is a complex problem that leads to pain and limited motions. Up to this time, no universal consensus has yet been reached as regards the optimal treatment for forearm instability. In some cases, conservative treatments are recommended for forearm instability injuries. However, quantitative studies on the conservative treatment of forearm instability are lacking. The present study developed a finite element model of the forearm to investigate the contribution of the distal radioulnar joint stabilizer on forearm stability. The stabilizer was designed to provide stability between the radius and ulna. The forearm model with and without the stabilizer was tested using the pure transverse separation and radial pull test for the different ligament sectioned models. The percentage contribution of the stabilizer and ligament structures resisting the load on the forearm was estimated. For the transverse stability of the forearm, the central band resisted approximately 50% of the total transverse load. In the longitudinal instability, the interosseous membrane resisted approximately 70% of the axial load. With the stabilizer, models showed that the stabilizer provided the transverse stability and resisted almost 1/4 of the total transverse load in the ligament sectioned models. The stabilizer provided transverse stability and reduced the loading on the ligaments. We suggested that a stabilizer can be applied in the conservative management of patients who do not have the gross longitudinal instability with the interosseous membrane and the triangular fibrocartilage complex disruption.

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Lateral collateral ligament deficiency of the elbow joint: A modeling approach

Cited by 10 publications

References 48 publications

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

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

Contribution of a distal radioulnar joint stabilizer on forearm stability: A modeling study

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