Artificial Intelligence in Trapeziometacarpal Joint Osteoarthritis: Improving Assessment and Clinical Decision-Making
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 2,500
- 试验地点
- 1
- 主要终点
- New TMC OA classification
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients will be included in the study if they have provided written general consent for the use of their clinical data and meet the requirements of one of the following two groups:
- •TMC OA group:
- •Patients who received a Touch implant for TMC joint arthroplasty and are documented in our clinical registry
- •Other clinical conditions:
- •Patients who underwent radiographic imaging for one of the following conditions, with a clearly visible TMC joint, for example: Distal radius fracture, carpal fracture, finger dislocation, or similar.
排除标准
- •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.
研究组 & 干预措施
Healthy
Patients with other clinical conditions leading to TMC joint radiography.
干预措施: No Intervention: Observational Cohort (Other)
Osteoarthritis
Patients in our TMC arthroplasty registry (Business Administration System for Ethics Committees (BASEC) number: 2019-02096): Patients who received a Touch implant for TMC joint arthroplasty. Data will be included in the study from the creation of the registry in 2018 until the end of January 2028.
干预措施: No Intervention: Observational Cohort (Other)
结局指标
主要结局
New TMC OA classification
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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.
次要结局
未报告次要终点