The Fore Foundation is a non-profit foundation based in Geneva. It was founded in 2013 by Dr. Lädermann, Chief Medical Officer and Director of the Orthopaedic Surgery and Traumatology of the Musculoskeletal System training programme at the Hôpital de la Tour in Geneva. The aim of the foundation is to promote and support basic and clinical research and teaching in the field of orthopaedic surgery, sports medicine, traumatology and imaging of the motor system. This project advances research, education and care to provide the best possible quality of life for people whose mobility is reduced due to injury, disease or age-related degeneration.
1. ARTIFICIAL INTELLIGENCE FOR QUALITY OF CARE – FOR BETTER DIAGNOSIS AND TREATMENT OF PATIENTS
Surgical repair of the rotator cuff is frequently indicated when non-operative treatment fails. However, the indication for surgery actually depends on hundreds of factors that are difficult to analyse adequately.
One solution regularly proposed to improve this selection is to launch numerous prospective comparative studies.
2. THE FORE FOUNDATION’S APPROACH.
Artificial intelligence (AI) has the potential to transform health and care institutions in their entire range of functions, from clinical to organisational and logistical aspects.
The objective of this project is to develop, initially for rotator cuff pathologies, a system comprising a prospective pre- and postoperative clinical database, to be implemented in an AI medical imaging viewer for automatic reading of magnetic resonance imaging.
3. PURPOSE AND ACTIVITIES
- Develop, train and compare machine learning algorithms based on several factors (demographic, clinical, operational…).
- Create AI prediction models for use in clinical practice and evaluate their accuracy.
- Show a significant decrease in the number of surgeries and healthcare costs.
- Determine effective surgical guidelines.
- Better advise patients of the potential risks and benefits of surgery.
- Demonstrate the effectiveness of AI-based technologies in developing predictive models for other clinically relevant outcome parameters.
- Deploy the developed platforms to other pathologies and medical-surgical specialties.