Anterolateral Ligament of the Knee: Diagnosis, Indications, Technique, Outcomes.

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Abstract

[Indexed for MEDLINE] 4. J Neuroeng Rehabil. 2026 Mar 11;23(1):130. doi: 10.1186/s12984-026-01939-2. AI-powered biomechanical modeling for ACL-reconstructed knees: predicting knee joint contact forces via computer vision and deep learning. Chen T(1), Zhou Z(1), Xu D(2), Yuan Y(3)(4), Zhou H(1)(4)(5), Ge Q(1), Jie T(1), Wang M(6), Xiang L(7), Fekete G(8), Ugbolue UC(9), Gu Y(10). Author information: (1)Faculty of Sport Science, Ningbo University, Ningbo, 315211, China. (2)Faculty of Sport Science, Ningbo University, Ningbo, 315211, China. xudatao@nbu.edu.cn. (3)Research Academy of Medicine Combining Sports, Ningbo No. 2 Hospital, Ningbo, 315010, China. (4)Sports Medicine Orthopedic Center, Ningbo No.2 Hospital, Ningbo, 315010, China. (5)Zhejiang Engineering Research Center for New Technologies and Applications of Helium-Free Magnetic Resonance Imaging, Ningbo, 315010, China. (6)Department of Biomedical Engineering, Faculty of Engineering, Hong Kong Polytechnic University, Hong Kong, 999077, China. (7)KTH Move Ability Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, 10044, Stockholm, Sweden. (8)Department of Materials Science and Technology, Audi Hungaria Faculty of Vehicle Engineering, Széchenyi István University, 9026, Győr, Hungary. (9)Biomechanics Laboratory, Division of Sports and Exercise, University of the West of Scotland, Glasgow, G11 5AA, UK. (10)Faculty of Sport Science, Ningbo University, Ningbo, 315211, China. guyaodong@nbu.edu.cn. BACKGROUND: Patients undergoing anterior cruciate ligament reconstruction (ACLR) are at high risk of osteoarthritis or secondary injuries, with abnormal knee contact forces (KCFs) identified as a key factor in joint degeneration. Traditional KCF assessment relies on expensive lab systems while advances in computer vision and AI now enable low-cost alternatives. However, currently available methods oversimplify knee mechanics and neglect compensatory movements, highlighting the urgent need for intelligent, real-time monitoring tools for personalized rehabilitation. Therefore, the aim of this study was to develop and validate an integrated, non-invasive framework for accurate KCFs prediction in ACLR patients during daily activities. We hypothesized that combining enhanced musculoskeletal modeling with a deep learning architecture incorporating spatiotemporal attention would improve the prediction accuracy across multiple movement tasks. METHODS: This study simultaneously recorded three daily movements of 29 post-ACLR patients using both Vicon and OpenCap. Motion trajectories captured by Vicon were imported into OpenSim for musculoskeletal modeling and KCFs calculation. Dataset comprising OpenCap-derived kinematics and OpenSim-computed KCFs was used to train 3 learning models for the prediction of KCFs in ACLR patients across different movements. RESULTS: Among three models, CNN-BiGRU-Attention model demonstrated the best predictive performance across all three movement tasks (R2walking = 0.973 ± 0.003, R2running = 0.982 ± 0.004, R2descending stairs = 0.951 ± 0.007). CNN and self-attention mechanism collectively enhanced the model's ability to capture key features in ACLR patients' movement data, thereby improving KCF prediction accuracy. Furthermore, for the three daily activities, all models showed superior KCFs prediction performance in running and stair-descent tasks compared to walking. CONCLUSION: 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. © 2026. The Author(s). DOI: 10.1186/s12984-026-01939-2 PMCID: PMC13093960