Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

Yan, Wen; Meng, Xiangjun; Sun, Jinglai; Yu, Hui; Wang, Zhi

doi:10.1186/s12880-021-00660-x

Cited by 3 publications

(1 citation statement)

References 38 publications

(32 reference statements)

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“…Different degrees of ATFL injuries correspond to different treatments and prognoses 30,36,37 . Therefore, accurate diagnosis of ATFL injury severity and selection of appropriate treatment methods are of importance for reconstructing ankle joint stability and preventing repeated sprains 38 . Our research results may be integrated with future research as part of an overall diagnostic assessment of lateral ankle instability.…”

Section: Discussionmentioning

confidence: 88%

Deep Learning Approach for MRI in the Classification of Anterior Talofibular Ligament Injuries

Chen

Zhao

et al. 2023

Magnetic Resonance Imaging

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BackgroundDiagnosing anterior talofibular ligament (ATFL) injuries differs among radiologists. Further assessment of ATFL tears is valuable for clinical decision‐making.PurposeTo establish a deep learning method for classifying ATFL injuries based on magnetic resonance imaging (MRI).Study TypeRetrospective.PopulationOne thousand seventy‐three patients from a single center with ankle MRI within 1 month of reference standard arthroscopy (in‐group dataset), were divided into training, validation, and test sets in a ratio of 8:1:1. Additionally, 167 patients from another center were used as an independent out‐group dataset.Field Strength/SequenceFat‐saturation proton density‐weighted fast spin–echo sequence at 1.5/3.0 T.AssessmentPatients were divided into normal, strain and degeneration, partial tear and complete tear groups (groups 0–3). The complete tear group was divided into five sub‐groups by location and the potential avulsion fracture (groups 3.1–3.5). All images were input into AlexNet, VGG11, Small‐Sample‐Attention Net (SSA‐Net), and SSA‐Net + Weight Loss for classification. The results were compared with four radiologists with 5–30 years of experience.Statistical TestsModel performance was evaluated by the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and so on. McNemar's test was used to compare performance among the different models, and between the radiologists and models. The intraclass correlation coefficient (ICC) was used to assess the reliability of the radiologists. P < 0.05 was considered statistically significant.ResultsThe average AUC of AlexNet, VGG11, SAA‐Net, and SSA‐Net + Weight Loss was 0.95, 0.99, 0.99, 0.99 in groups 0–3 and 0.96, 0.99, 0.99, 0.99 in groups 3.1–3.5. The effect of SSA‐Net + Weight Loss was similar to SSA‐Net but better than AlexNet and VGG11. In the out‐group test set, the AUC of SSA‐Net + Weight Loss ranged from 0.89 to 0.99. The ICC of radiologists was 0.97–1.00. The effect of SSA‐Net + Weight Loss was better than each radiologist in the in‐group and out‐group test sets.Data ConclusionDeep learning has potential to be used for classifying ATFL injuries. SSA‐Net + Weight Loss has a better diagnostic effect than radiologists with different experience levels.Level of Evidence4Technical EfficacyStage 2

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Section: Discussionmentioning

confidence: 88%

Deep Learning Approach for MRI in the Classification of Anterior Talofibular Ligament Injuries

Chen

Zhao

et al. 2023

Magnetic Resonance Imaging

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The increased anterior talofibular ligament–posterior talofibular ligament angle on MRI may help evaluate chronic ankle instability

et al. 2023

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Purpose This study intended to compare the difference between the anterior talofibular ligament (ATFL) and posterior talofibular ligament (PTFL) angle with chronic ankle instability (CAI) patients and healthy volunteers, and to confirm whether using the ATFL–PTFL angle could be a reliable assessment method for CAI, so as to improve the accuracy and specificity of clinical diagnosis. Methods This retrospective study included 240 participants: 120 CAI patients and 120 healthy volunteers between 2015 and 2021. The ATFL–PTFL angle of the ankle region was gaged in the cross-sectional supine position on MRI between two groups. After participants undergoing a comprehensive MRI scanning, ATFL–PTFL angles were regarded as the main indicator of patients with the injured ATFLs and healthy volunteers to compare, and were measured by an experienced musculoskeletal radiologist. Moreover, other qualitative and quantitative indicators referring to anatomical and morphological characteristics of the AFTL were included in this study with MRI, such as the length, width, thickness, shape, continuity, and signal intensity of the ATFL, which can be used as secondary indicators. Results In the CAI group, the ATFL–PTFL angle was 90.8° ± 5.7°, which was significantly different from the non-CAI group where the ATFL–PTFL angle for 80.0° ± 3.7° (p < 0.001). As for the ATFL-MRI characteristics, the length (p = 0.003), width (p < 0.001), and thickness (p < 0.001) in the CAI group were also significantly different from the non-CAI group. Over 90% of the cases, patients of the CAI group had injured ATFL with an irregular shape, non-continuous, and high or mixed signal intensity. Conclusion Compared with healthy people, the ATFL–PTFL angle of most CAI patients is larger, which can be used as a secondary index to diagnose CAI. However, the MRI characteristic changes of ATFL may not relate to the increased ATFL–PTFL angle.

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The Protective Effect of Ginsenoside Rg1 on Apoptosis in Human Ankle Joint Traumatic Arthritis Chondrocytes

Shen

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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The ankle biomechanics is easily changed due to the acute injury of the tissue around the ankle joint and the damage of the ankle joint structure, such as ankle instability and joint surface imbalance. When the mechanical load of the ankle changes, it can cause ankle regeneration and remodeling processes such as cartilage loss, bone remodeling, and degenerative changes. The aim of this study was to investigate the effect and mechanism of ginsenoside Rg1 against interleukin-1β (IL-1β)-induced apoptosis in human articular chondrocytes (HACs). The apoptosis model of HAC cells was established by IL-1β induction, and then the HAC cells were cultured with different concentrations of Rg1. The protective effect of Rg1 on HAC cell apoptosis was investigated by detecting the changes of apoptosis and activity of PI3K/Akt/mitochondrial signaling pathway. The results showed that a specific concentration of Rg1 could promote the proliferation of IL-1β-induced HAC cells and inhibit apoptosis. At the same time, Rg1 treatment with specific concentration can reduce the content of reactive oxygen species (ROS) and malondialdehyde (MDA) in HACs and improve the related expression of mitochondrial membrane potential (MMP). Furthermore, qRT-PCR and western blot results showed that Rg1 could improve the low expression of Bcl-2 and inhibit the high expression of Bax, caspase-3, caspase-8, caspase-9, FasL, AIF, and Cyto c in IL-1β-induced cells. In summary, Rg1 can inhibit IL-1β-induced apoptosis of HAC cells by decreasing the activity of PI3K/Akt/mitochondrial signaling pathway, and Rg1 has a protective effect on apoptosis of HAC cells.

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Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

Cited by 3 publications

References 38 publications

Deep Learning Approach for MRI in the Classification of Anterior Talofibular Ligament Injuries

Deep Learning Approach for MRI in the Classification of Anterior Talofibular Ligament Injuries

The increased anterior talofibular ligament–posterior talofibular ligament angle on MRI may help evaluate chronic ankle instability

The Protective Effect of Ginsenoside Rg1 on Apoptosis in Human Ankle Joint Traumatic Arthritis Chondrocytes

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