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Updated: Sep 8, 2026
Non-Invasive Compression-Induced Anterior Cruciate Ligament (ACL) Injury and In Vivo Imaging of Protease Activity in Mice
Published on: September 29, 2023
KneeFusionNet Enables Accurate and Efficient Comprehensive Detection of Knee Ligament Injuries on Magnetic Resonance
Bo Chen1,2, Xiongfeng Tang1,2,3, Deming Guo1,2
1Department of Sports Medicine and Arthroscopy, The Second Hospital of Jilin University, Changchun, China.
Purpose:
To develop and externally validate KneeFusionNet, a multimodal deep learning model for detecting anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), medial collateral ligament (MCL), and lateral collateral ligament (LCL) injuries on knee magnetic resonance imaging (MRI), and to assess the impact of multimodal fusion and artificial intelligence (AI) assistance on diagnostic performance.
Methods:
This 3-center retrospective study was conducted between April 2020 and August 2025. The injury group included patients who underwent knee MRI within 1 month before arthroscopy and had surgically confirmed ACL, PCL, MCL, or LCL injuries; controls had unremarkable MRI and physical examination findings. Two centers formed the development set, and the remaining center served as the external test set. DenseNet-based KneeFusionNet was developed and compared with 3 deep learning models. Diagnostic performance was assessed using the area under the receiver operating characteristic curve, and a reader study evaluated AI-assisted diagnostic performance.
Results:
Overall, 919 patients were included: 759 in the development set and 160 in the external test set. Multimodal fusion outperformed single-modality approaches for all ligaments (all P < .05). On internal validation, KneeFusionNet achieved area under the receiver operating characteristic curves of 0.971 for ACL, 0.906 for PCL, 0.919 for MCL, and 0.924 for LCL. Corresponding external area under the receiver operating characteristic curves were 0.888, 0.867, 0.862, and 0.874. Sex-stratified analyses showed no consistent sex-related decrease in model performance. KneeFusionNet outperformed all comparison models on internal validation (all P < .05). AI assistance improved mean diagnostic accuracy for junior surgeons from 0.818 to 0.900 and reduced mean interpretation time by 14.73 seconds across all surgeons (all P < .05).
Conclusions:
KneeFusionNet detected ACL, PCL, MCL, and LCL injuries on MRI with high diagnostic performance and outperformed comparison models. AI assistance improved diagnostic accuracy for junior surgeons and reduced interpretation time for all surgeons.
Level Of Evidence:
Level III, retrospective case-control study.
