Musculoskeletal disorders are the second most common cause of disability worldwide. With an ageing population, these conditions represent one of the greatest future challenges for health and societal welfare.
The main goal of CIM is to bring together experts from complementary disciplines to develop, evaluate, and implement effective, innovative, and practical solutions to the challenges we face in the musculoskeletal field.
We aim to explore a range of AI-based technological approaches to improve current understanding of disease progression and the risk of persistent conditions.
CIM is organized into four pillars based on research methods:
- Epidemiological research using large datasets
- AI research in medicine and health
- Clinical studies and intervention research
- Qualitative methods in health research
In addition, we have established two thematic groups that cut across all three research areas:
- User involvement group
- Research-based innovation
Head of research group
Heads
Head of pillar I
Bjørnar Berg
Head of pillar II
Head of pillar III
Head of pillar IV
Members
Projects
- Adolescent Idiopathic Scoliosis in a life-course perspective (AIS-Life)
- Adulthood healthcare use for co-occurring pain and mental distress – the roles of early life adversity and buffers (PAINMENT)
- A healthy back throughout life – a digital information bank (The BACK Bank)
- AI-driven patient avatar for physiotherapy education
- Applying Artificial Intelligence in Developing Personalized and Sustainable Healthcare for Spinal Disorders (AID-Spine, part I)
- Can Motivational Interviewing facilitate return to work in sick-listed people with musculoskeletal disorders? (MI-NAV)
- Co-occurrent pain and psychological distress: From adolescence to adulthood
- Designing an intelligent personalized vest for scoliosis brace to improve treatment and monitoring of scoliosis in adolescents
- Health in young adults (HEYoung)
- HEYoung intervention study
- Implementing a stratified vocational advice intervention in individuals at high risk of long-term sickness absence with musculoskeletal disorders (SVAI)
- Machine learning for the analysis of physiotherapy records
- Physiotherapy for non-surgical spine patients
- Promoting and inhibiting factors for education and employment among young adults receiving welfare benefits or health services due to persistent pain and psychological distress
- Somatocognitive therapy in treatment of provoked localized vulvodynia (ProLove)
- The Back Complaints in the Elders, Norway (BACE-N)
- Young user involvement group in musculoskeletal health
Blog
Collaboration
CIM is a strategic research group collaborating closely with the research group Applied Artificial Intelligence.