Abstract
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.
Highlights
Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively
The last decade, significant progress in the development of computational tools to study the complex biomechanical factors involved in OA was made, using computational models
In terms of modeling approaches, finite element models have been quite promising in predicting degeneration of different constituents of articular cartilage (Hosseini et al, 2014; Mononen et al, 2016) and the role of the subchondral bone in the disease process
Summary
Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. The response of the chondrocytes in the cartilage and the underlying bone is different for different joints (Kuettner and Cole, 2005), which can explain the susceptibility of certain joints to OA as compared to others. It is already evident from the discussion above that in order to fully understand the onset and progression of OA, it is important to understand the biomechanical environment of anatomically complex joint structures like the knee joint. Some generalizable conclusions are drawn and suggestions for future work on further exploitation and integration of the different models are made
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