New Techniques for Modeling, Prognosis, Diagnosis, and Treatment of Human Knee Pathology

The knee joint is one of the largest and most powerful joints of the human body since it supports enormous loads and pressures while offering stable and flexible movements. For this reason, the lower limb is vulnerable to the appearance of degenerative diseases, such as femoro-tibial osteoarthritis (OA) that dramatically reduces the quality of life in a patient. Knee osteoarthritis represents an enormous public health problem for patients and imposes a significant economic burden. Knee OA leads to annual healthcare expenses of more than $ 27 billion with gross knee arthroplasty budgets reaching $ 11 billion annually. Developing new prognostic technology and methodology could contribute to the detection of this disease and reduce human and economic losses.

The objective of this research topic is to collect recent studies on the development of new techniques for the prognosis, prediction, diagnosis, and modeling of human knee diseases, in particular osteoarthritis. The change of biomechanics induced by a knee arthroplasty and its effect on knee performance is also part of the research project proposal.

It is the use of innovative approaches and methodology based on artificial intelligence, medical imaging, the finite element method, texture method, datasets, or a combination of these techniques to detect, predict and treat osteoarthritis of knee before its onset or at least in a potentially reversible stage that will allow the correct diagnosis of the state of the knee and the disregard of cartilage or other components of the knee joint and to propose the appropriate treatment and avoid surgical operations.

Any contributions in the following areas are welcome:

- Modeling of knee biomechanical behavior

- Modeling of knee diseases, treatments, and/or devices

- Prognosis and prediction of knee osteoarthritis

- Preoperative knee planning

- Computer-assisted knee surgery