Artificial Intelligence in Trapeziometacarpal Joint Osteoarthritis: Improving Assessment and Clinical Decision-Making

This is a retrospective cohort study utilizing radiographic and computed tomography (CT) imaging data collected during routine clinical care at Schulthess Klinik Zürich. The study focuses on developing and validating artificial intelligence (AI)-based tools for the assessment of trapeziometacarpal (TMC) joint osteoarthritis (OA) and implant monitoring. The project is divided into four subprojects: (1) development of a new radiographic classification system for TMC OA, (2) automation of the classification using deep learning, (3) automated detection of implant migration, and (4) 3-dimensional (3D) reconstruction of the TMC joint from biplanar radiographs. Data will be sourced from two cohorts: patients from our clinical TMC arthroplasty registry who received the Touch implant, and patients with other wrist-related conditions who underwent radiographic imaging with a visible TMC joint. Together, these cohorts provide a broad coverage across the full spectrum of OA severity. OA-related features and implant related features will serve as the foundation for model training and validation. Also, they will be validated with CT images regarding reliability and accuracy. The resulting prototypes for automated OA staging, implant migration detection, and 3D modeling of the TMC joint are exclusively used for research purposes. Any future clinical use of these tools, including evaluation under Swissmedic (Swiss Agency for Therapeutic Products) regulations, will be part of a separate project.