AI-powered biomechanical modeling for ACL-reconstructed knees: predicting knee joint contact forces via computer vision and deep learning
The developed framework successfully achieved high-precision prediction of KCFs. This technological breakthrough not only provides a real-time quantitative tool for rehabilitation monitoring in patients with ACLR, but also facilitates a paradigm shift from static laboratory analysis to dynamic real-time monitoring, with broad application prospects in sports medicine, rehabilitation engineering.
Authors
- Qincheng Ge
- Tianle Jie (ORCID: https://orcid.org/0000-0001-9227-1585)
- Zhifeng Zhou
- Ukadike Chris Ugbolue
- Yi Yuan
- Yaodong Gu
- Meizi Wang
- Liangliang Xiang
- Huiyu Zhou
- Datao Xu
- Tianxiao Chen
- Gusztáv Fekete
Institutions
- Ningbo University (CN)
- Hong Kong Polytechnic University (HK)
- Imaging, Brain, and Neuropsychiatry (FR)
- Ningbo No. 2 Hospital (CN)
- Ningbo No.6 Hospital (CN)
- University of Glasgow (GB)
- Széchenyi István University (HU)
- KTH Royal Institute of Technology (SE)
Publication Details
- Journal
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2026-03-11
- DOI
- https://doi.org/10.1186/s12984-026-01939-2
- Primary Topic
- Knee injuries and reconstruction techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00