Project description
AI steps toward knee osteoarthritis treatment
Knee osteoarthritis (KOA) affects around 14 % of Europeans over 40, with no cure or reliable prevention available. Current rehabilitation methods, based on indirect knee biomechanics, often fall short. However, the EU-funded CARE-KNEEOA project aims to change that. Supported by the Marie Skłodowska-Curie Actions programme, the project is developing personalised, AI-assisted computational models that predict knee cartilage degeneration and regeneration. These models, designed for clinical use, will be fast, automated, and capable of using out-of-lab motion data. By simulating knee mechanics in real-life activities, CARE-KNEEOA hopes to revolutionise KOA treatment, improving patient outcomes and potentially reducing the need for surgery.
Objective
Knee osteoarthritis (KOA) is a leading cause of disability worldwide, with ~14% prevalence in Europeans aged over 40. KOA prevalence continues to rise, thus far, with no cure or proven prevention protocols. Nonetheless, an aberrant knee mechanobiological environment is known to accelerate KOA development. Tailored rehabilitation, aiming to favorably alter knee biomechanics and restore the joint, has shown great potential to postpone or decelerate KOA progression. But current rehabilitation protocols are based on indirect measures of knee biomechanics, often leading to suboptimal outcomes. Computational models have offered great potential for simulating knee mechanical response in functional activities, though none are developed in a holistic and individualized context. More importantly, they lack the prediction capability of tissue degeneration/regeneration to loading and the potential for clinical use, i.e. are not automated and fast and cannot use out-of-lab motion data. In this project, I will develop and validate highly personalized in silico tools to quantify knee cartilage mechanobiological degenerative/regenerative response geared towards out-of-lab and clinical use for predicting KOA progression in different functional activities, allowing personalized rehabilitation. The multiphysics computational models, assisted with artificial intelligence (AI), will be validated at different spatial scales using in vitro tissue and cell level experiments and in vivo joint loading and quantitative medical images. This multidisciplinary project bridges together complementary skill sets of Dr. Esrafilian, Profs. Korhonens and Delps teams, with their expertise in biomechanics, computational modeling, biochemistry, biology, and AI. The beyond state-of-the-art models of this research can make a profound impact on early-stage KOA prediction and treatment planning, potentially increasing the quality of life in KOA individuals and reducing the need for surgical interventions.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesbiochemistry
- medical and health sciencesclinical medicinephysiotherapy
- natural sciencesbiological sciencesbiophysics
- natural sciencescomputer and information sciencescomputational sciencemultiphysics
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinator
70211 KUOPIO
Finland