Use of Computational Modeling to Study Joint Degeneration: A Review.

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient's individualized risk assessment as screening tool for use in clinical practice.

Author Correction: Mechanical Metrics of the Proximal Tibia are Precise and Differentiate Osteoarthritic and Normal Knees: A Finite Element Study.

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Mechanical Metrics of the Proximal Tibia are Precise and Differentiate Osteoarthritic and Normal Knees: A Finite Element Study.

Our objective was to identify precise mechanical metrics of the proximal tibia which differentiated OA and normal knees. We developed subject-specific FE models for 14 participants (7 OA, 7 normal) who were imaged three times each for assessing precision (repeatability). We assessed various mechanical metrics (minimum principal and von Mises stress and strain as well as structural stiffness) across the proximal tibia for each subject. In vivo precision of these mechanical metrics was assessed using CV%RMS. We performed parametric and non-parametric statistical analyses and determined Cohen's d effect sizes to explore differences between OA and normal knees. For all FE-based mechanical metrics, average CV%RMS was less than 6%. Minimum principal stress was, on average, 75% higher in OA versus normal knees while minimum principal strain values did not differ. No difference was observed in structural stiffness. FE modeling could precisely quantify and differentiate mechanical metrics variations in normal and OA knees, in vivo. This study suggests that bone stress patterns may be important for understanding OA pathogenesis at the knee.

On prediction of the strength levels and failure patterns of human vertebrae using quantitative computed tomography (QCT)-based finite element method.

This paper presents an effective patient-specific approach for prediction of failure initiation and growth in human vertebra using the general framework of the quantitative computed tomography (QCT)-based finite element method (FEM). The studies were carried out on 13 vertebrae (lumbar and thoracic), excised from 3 cadavers with the average age of 42 years old. Initially, 4 samples were QCT scanned and the images were directly converted into voxel-based 3D finite element models for linear and nonlinear analyses. The equivalent plastic strains obtained from the nonlinear analyses were used to predict the occurrence of local failures and development of the failure patterns. In the linear analyses, the strain energy density measure was used to identify the critical elements and predict the failure patterns. Subsequently, the samples were destructively tested in uniaxial compression and the experimental load-displacement diagrams were obtained. The plain radiographic images of the tested samples were also examined for observation of the failure patterns. In continuation, the presence of osteolytic defects in vertebrae was simulated by creation of artificial cavities within 9 remaining samples using a computer numerical control (CNC) milling machine. The same protocol was followed for scanning, modeling, and destructive testing of these samples. A strong correlation was found between the predicted and measured strengths. Finally, a typical vertebroplasty treatment was simulated by injection of low-viscosity bone cement within 3 compressed samples. The failure patterns and the associated load levels for these samples were also predicted using the QCT voxel-based FEM.