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Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear

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Abstract

Anterior cruciate ligament (ACL) tears are prevalent orthopedic sports injuries and are difficult to precisely classify. Previous works have demonstrated the ability of deep learning (DL) to provide support for clinicians in ACL tear classification scenarios, but it requires a large quantity of labeled samples and incurs a high computational expense. This study aims to overcome the challenges brought by small and imbalanced data and achieve fast and accurate ACL tear classification based on magnetic resonance imaging (MRI) of the knee. We propose a lightweight attentive graph neural network (GNN) with a conditional random field (CRF), named the ACGNN, to classify ACL ruptures in knee MR images. A metric-based meta-learning strategy is introduced to conduct independent testing through multiple node classification tasks. We design a lightweight feature embedding network using a feature-based knowledge distillation method to extract features from the given images. Then, GNN layers are used to find the dependencies between samples and complete the classification process. The CRF is incorporated into each GNN layer to refine the affinities. To mitigate oversmoothing and overfitting issues, we apply self-boosting attention, node attention, and memory attention for graph initialization, node updating, and correlation across graph layers, respectively. Experiments demonstrated that our model provided excellent performance on both oblique coronal data and sagittal data with accuracies of 92.94% and 91.92%, respectively. Notably, our proposed method exhibited comparable performance to that of orthopedic surgeons during an internal clinical validation. This work shows the potential of our method to advance ACL diagnosis and facilitates the development of computer-aided diagnosis methods for use in clinical practice.

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Data Availability

The dataset analyzed during the current study are not publicly available due to data privacy but are available from the corresponding author on reasonable request.

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Funding

This work was supported by the Natural Science Foundation of Hunan Province China under Grant 2022JJ30673; National Natural Science Foundation of China (No. 81802208) and the Foundation of Health Commission of Hunan Province (No. 202204074821); the Wisdom Accumulation and Talent Cultivation Project of the Third Xiangya Hospital of Central South University (YX202209) fund this study.

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Author notes

  1. Jiaoju Wang and Jiewen Luo contributed equally to this work.

Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Changsha, 410083, Hunan, China

    Jiaoju Wang, Alphonse Houssou Hounye & Muzhou Hou

  2. Department of Orthopaedic Surgery, Third Xiangya Hospital of Central South University, Changsha, 410013, Hunan, China

    Jiewen Luo, Jiehui Liang, Yangbo Cao, Jing Feng, Lingjie Tan & Jinshen He

  3. Department of Orthopaedic Surgery, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750021, Ningxia Hui Autonomous Region, China

    Zhengcheng Wang

  4. School of Civil Engineeringand Architecture, Nanyang Normal University, Nanyang, 473061, Henan, China

    Jingming Li

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  1. Jiaoju Wang
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Contributions

Jiaoju Wang and Jiewen Luo: conceptualization, methodology, software, formal analysis, writing—original draft and optimation of methodology. Jiehui Liang, Yangbo Cao, Jing Feng, Lingjie Tan and Zhengcheng Wang: clinical validation. Jingming Li and Alphonse Houssou Hounye: writing—review and editing. Muzhou Hou and Jinshen He: financial support, investigation, resources, writing—review and editing.

Corresponding authors

Correspondence to Muzhou Hou or Jinshen He.

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This study is approved by the ethical standards of the Medical Ethics Committee of Third Xiangya Hospital of Central South University (ID: 2021-s229).

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Wang, J., Luo, J., Liang, J. et al. Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear. J Digit Imaging. Inform. med. 37, 688–705 (2024). https://doi.org/10.1007/s10278-023-00944-4

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