End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI

Dung, Nguyen Tan; Thuan, Ngo Huu; Dung, Truong Van; Nho, Le Van; Trí, Nguyễn Minh; Vy, Vu Pham Thao; Hoang, Le Ngoc; Phat, Nguyen Thuan; Chuong, Dang Anh; Dang, Luong Huu

doi:10.1016/j.diii.2022.10.010

Cited by 8 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, there has been no deep learning research on the ATFL. Research on ligaments is mainly focused on the anterior cruciate ligament (ACL) of the knee joint, and our research results are better than the results of deep learning research on the ACL 39,40 …”

Section: Discussionmentioning

confidence: 72%

“…Research on ligaments is mainly focused on the anterior cruciate ligament (ACL) of the knee joint, and our research results are better than the results of deep learning research on the ACL. 39,40 In this study, the SSA-Net + Weight Loss deep learning model was compared with MSK radiologists, and the differences between the four radiologists and the model were statistically significant in both the in-group and out-group test sets. For inexperienced doctors, non-MSK radiologists or community doctors, this model has potential to evaluate the ATFL independently and quickly, which may be helpful for more detailed and accurate diagnosis.…”

Section: Discussionmentioning

confidence: 97%

See 1 more Smart Citation

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

Chen

Zhao

et al. 2023

Magnetic Resonance Imaging

View full text Add to dashboard Cite

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

show abstract

Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 97%

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

Chen

Zhao

et al. 2023

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…In the realm of musculoskeletal imaging, several studies have addressed specific pathologies. Dung et al [36] and Ni et al [37] proposed approaches for classifying anterior cruciate ligament tears and diagnosing anterior talofibular ligament tears, respectively, in 2023. In a different study, Zhao et al [11] attempted to use a method based on natural image processing to capture clinically important details with limited annotations, but it did not achieve the desired outcome.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Learning-Based Meniscus Tear Detection From Accelerated MRI

Harman,

Selver,

Baris

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Magnetic Resonance Imaging (MRI) is an exceptional diagnostic tool known for its ability to provide superior soft tissue contrast. Despite its proven efficacy, traditional MRI methods have some inherent limitations, including relatively longer scan times and the requirement for specialized expertise in data analysis. These factors can, at times, create challenges in the widespread adoption of MRI in certain research and clinical scenarios. Deep learning (DL) methods for MRI reconstruction and analysis offer a promising solution to address this issue. While these DL techniques have been validated using standard image quality metrics, they fall short in assessing clinically relevant details. This hinders their clinical reliability and practical application. Although radiologists have previously compared the diagnostic equivalence of accelerated DL-reconstructed images to conventional ones for evaluating knee internal derangement, it remains uncertain whether DL detectors can accurately identify clinically important details when reconstruction models are used. Furthermore, the artifacts or hallucinations generated by DL reconstructions in knee MRI have not been examined in a clinical setting. Thus, this study aims to determine the performance of DL detectors on retrospectively accelerated DL-reconstructed knee MRI, comparing them to conventional imaging and expert evaluations for detecting meniscal tears. Our investigation yields three significant contributions. First, an in-depth analysis of DL reconstruction highlights the presence of hallucinations in the femur, tibia, and false positive artifacts, indicating that the overall reconstruction quality does not directly affect pathological features. Second, the results of DL detectors demonstrate that their performance aligns well with image quality assessment metrics and expert scores. This finding validates the reliability of the detection outcomes. Finally, we propose an integrated (i.e. reconstruction + detection) process for meniscal tears on fastMRI+ data and achieved state-of-the-art results with average precision scores of 0.69 and 0.67 at 4− and 8-fold accelerations, respectively.INDEX TERMS MRI reconstruction, meniscal tear detection, and deep learning.

show abstract

Application of Artificial Intelligence in Musculoskeletal Imaging

Ni,

Yuan

2024

Artificial Intelligence in Medical Imaging in China

View full text Add to dashboard Cite

End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI

Cited by 8 publications

References 22 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

Deep Learning-Based Meniscus Tear Detection From Accelerated MRI

Application of Artificial Intelligence in Musculoskeletal Imaging

Contact Info

Product

Resources

About