Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Predictive Biomechanics of Knee Osteoarthritis: Imaging, Modelling, Simulation and AI-Driven Prognostics

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is pleased to welcome you to submit your research to the Article Collection "Predictive Biomechanics of Knee Osteoarthritis: Imaging, Modelling, Simulation and AI-Driven Prognostics".

Knee osteoarthritis (KOA) is one of the most prevalent musculoskeletal disorders worldwide, characterized by the progressive degeneration of joint tissues and leading to pain, disability, and reduced quality of life. Mechanical loading, particularly repetitive and cyclic loading associated with daily activities, plays a central role in the initiation and progression of the disease. In recent years, advances in computational biomechanics have enabled the development of predictive models capable of simulating joint behavior under physiological and pathological conditions. Combined with the rapid evolution of medical imaging and artificial intelligence (AI), these approaches are transforming our ability to analyze, visualize, and predict the progression of knee osteoarthritis. This Article Collection aims to bring together cutting-edge research at the intersection of imaging, modeling, simulation, and AI-driven prognostics to advance the field of predictive biomechanics of KOA.

Understanding and predicting the progression of knee osteoarthritis remains a major scientific and clinical challenge. Current diagnostic approaches are often limited to late-stage observations, while treatment strategies are generally reactive rather than preventive. The integration of imaging data, biomechanical modeling, and AI offers a paradigm shift toward early diagnosis, personalized prognosis, and decision support in clinical practice. Predictive biomechanics enables the identification of key mechanical and biological factors driving disease evolution, while AI techniques can leverage large datasets to uncover complex patterns and improve prediction accuracy. Such advances are crucial not only for improving patient-specific treatment planning and rehabilitation strategies but also for guiding the design of implants, prostheses, and preventive interventions. Ultimately, this integrated approach has the potential to reduce healthcare costs and significantly enhance patient outcomes.

This Collection welcomes contributions addressing the development and application of advanced computational and imaging-based methods for the study and prediction of knee osteoarthritis. Topics of interest include, but are not limited to:

• Multiscale and patient-specific biomechanical modeling of the knee joint
• Finite element and data-driven simulations under cyclic loading
• Medical imaging techniques for cartilage, bone, and soft tissue characterization
• Image-based model reconstruction and validation
• AI and machine learning approaches for disease prediction and progression analysis
• Digital twins and personalized medicine
• Visualization techniques for biomechanical data
• Integration of experimental and computational approaches.

Submissions focusing on methodological innovations, clinical applications, and translational research are particularly encouraged. The Collection will consider original research articles, review papers, and methodological studies in line with the journal’s scope, with an emphasis on high-quality, rigorously validated contributions.

Meet the Guest Advisors

Prof. Abdelwahed Barkaoui is a full Professor of Mechanics and Dean of ECINE at the International University of Rabat. His research focuses on biomechanics and mechanobiology, with an emphasis on multiscale modelling, numerical simulation, and artificial intelligence for musculoskeletal disease prognostics. He is actively involved in international research projects and the development of innovative solutions in biomedical engineering.

Prof. Patrick Chabrand is Emeritus Professor of Universities at Aix-Marseille University and a senior researcher at the Institute of Movement Sciences (UMR 7287). His work integrates osteoarticular biomechanics, continuum and solid mechanics, biomaterials, and numerical methods to advance precision diagnosis, implant design, and rehabilitation.

The Guest Advisors declare no conflict of interest regarding this work.