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

Not yet recruiting

• Conditions: Thumb Osteoarthritis

• Registration Number: NCT07148349
• Lead Sponsor: Schulthess Klinik

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-08
• Study First Submitted: 2025-08-21
• Study First Submitted QC: 2025-08-21
• Study First Posted: 2025-08-29
• Last Update Submitted: 2025-08-29
• Study Start: 2025-09-01
• Last Update Posted: 2025-09-08
• Primary Completion: 2028-08
• Study Completion: 2028-08-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 2500

Inclusion Criteria

Not provided

Exclusion Criteria

The exclusion criteria are defined based on the two inclusion groups:

TMC OA group:

• Patients with posttraumatic OA.
• Patients with rheumatoid arthritis.

Other clinical conditions:

• Eaton Littler score higher than one.
• Prior surgery affecting the trapezium or the first metacarpal.
• Clinical condition resulting in abnormal TMC joint morphology.

Furthermore, patients will also be excluded if they revoke their consent in writing or via verbal withdrawal.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
New TMC OA classification	Preoperative	Create a novel classification system for TMC OA based on joint and bone features in X-rays. This system should ensure better reliability, accurately reflect joint health, and provide clinically relevant insights for treatment decisions.
Automation of the new TMC OA classification	Preoperative	Implement a deep learning-based system to automate TMC joint OA classification in plain radiographs using convolutional neural networks for segmentation and feature extraction. This approach aims to improve consistency and reduce the need for manual annotations.
Automated implant migration detection	Postoperative	Develop an AI-based system for detecting implant loosening and migration in postoperative radiographs, for early detection of loosening and long-term monitoring of implant stability.
3D reconstruction of the TMC joint	Preoperative	Create a 3D reconstruction of the trapezium and the first metacarpal from biplanar radiographs. This provides advanced insights into individual trapezium geometry to improve surgical planning.

Trial Locations

Locations (1)

Schulthess Klinik; 🇨🇭 Zurich, Canton of Zurich, Switzerland