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Artificial Intelligence Models for Classifying Wrist Ligament Injuries Using Synthetically-Generated Joint Proximity
Hsuan-Yu Chen1, Jon Camp2, Taylor P Trentadue3,4,5
1Orthopedic Biomechanics Research Laboratory, Department of Orthopedics, Mayo Clinic, Rochester, MN, USA.
Biorxiv : the Preprint Server for Biology
|June 29, 2026
Summary
Artificial intelligence (AI) models trained on synthetic data from finite element models (FEMs) show promise for diagnosing wrist ligament injuries. This approach uses interosseous proximity maps to improve noninvasive diagnostic accuracy.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Artificial Intelligence
Background:
- Diagnosing wrist ligament injuries is complex, necessitating early detection to prevent osteoarthritis.
- Interosseous proximity maps from volumetric imaging offer insights into wrist joint health.
- Artificial intelligence (AI) can potentially improve noninvasive diagnosis using imaging metrics.
Purpose of the Study:
- To demonstrate the feasibility of training AI models using synthetic data.
- To generate synthetic interosseous proximity map data from finite element models (FEMs).
- To develop AI models for classifying wrist ligament injuries.
Main Methods:
- Personalized wrist FEMs were created from 4D CT data.
- 7,500 injury scenarios generated 9 million synthetic RGB images of proximity vector fields.
- Mixed-input convolutional neural networks (CNNs) were developed and evaluated.
Main Results:
- CNNs achieved an average AUROC of 0.757 across all injury types.
- Performance improved to an average AUROC of 0.824 for clinically relevant angles.
- High sensitivities and specificities (>0.99) were observed in specific simulations.
Conclusions:
- Synthetic FEM data can effectively train AI for wrist ligament injury classification.
- Proximity-based RGB images show potential as biomarkers for ligamentous injury.
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