Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear.
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 labe...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 688 - 706 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177625994&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177625994 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177625994 177625994 177625994 10.1007/s10278-023-00944-4 177625994 ppf: 688 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear. aug: au: Wang, Jiaoju Luo, Jiewen Liang, Jiehui Cao, Yangbo Feng, Jing Tan, Lingjie Wang, Zhengcheng Li, Jingming Hounye, Alphonse Houssou Hou, Muzhou He, Jinshen affil: https://ror.org/00f1zfq44 School of Mathematics and Statistics, Central South University, 410083, Changsha, Hunan, China sug: subj: Anterior Cruciate Ligament Injuries Diagnosis Anterior Cruciate Ligament Injuries Radiography Anterior Cruciate Ligament Injuries Classification Magnetic Resonance Imaging Knee Radiography Neural Networks (Computer) Natural Language Processing Human Funding Source Deep Learning Descriptive Statistics Data Analysis Software Data Mining Male Female Adult China Kruskal-Wallis Test Chi Square Test Pearson's Correlation Coefficient Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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