ArticleBMC musculoskeletal disorders2022
Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image.
Article in BMC musculoskeletal disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- MRI deep learning models for assisted diagnosis of knee pathologies: a systematic review.European radiology · 2025Pooled it
- Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance.PloS one · 2025Pooled it
- Review
- Deep learning algorithm for the automatic assessment of axial vertebral rotation in patients with scoliosis using the Nash-Moe method.Scientific reports · 2025Article
- Artificial intelligence in musculoskeletal radiology: practical aspects and latest perspectives.BJR open · 2025Review
- MV2SwimNet: A lightweight transformer-based hybrid model for knee meniscus tears detection.PloS one · 2025Article
- Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Review
- A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements.World journal of clinical cases · 2023Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Feasibility of the fat-suppression image-subtraction method using deep learning for abnormality detection on knee MRI.Polish journal of radiology · 2023Article
- Use of Machine Learning in Stroke Rehabilitation: A Narrative Review.Brain & NeuroRehabilitation · 2022Review
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5 authors.
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Abstract
backgroundDeep learning (DL) is an advanced machine learning approach used in diverse areas, such as image analysis, bioinformatics, and natural language processing. A convolutional neural network (CNN) is a representative DL model that is advantageous for image recognition and classification. In this study, we aimed to develop a CNN to detect meniscal tears and classify tear types using coronal and sagittal magnetic resonance (MR) images of each patient.
methodsWe retrospectively collected 599 cases (medial meniscus tear = 384, lateral meniscus tear = 167, and medial and lateral meniscus tear = 48) of knee MR images from patients with meniscal tears and 449 cases of knee MR images from patients without meniscal tears. To develop the DL model for evaluating the presence of meniscal tears, all the collected knee MR images of 1048 cases were used. To develop the DL model for evaluating the type of meniscal tear, 538 cases with meniscal tears (horizontal tear = 268, complex tear = 147, radial tear = 48, and longitudinal tear = 75) and 449 cases without meniscal tears were used. Additionally, a CNN algorithm was used. To measure the model's performance, 70% of the included data were randomly assigned to the training set, and the remaining 30% were assigned to the test set.
resultsThe area under the curves (AUCs) of our model were 0.889, 0.817, and 0.924 for medial meniscal tears, lateral meniscal tears, and medial and lateral meniscal tears, respectively. The AUCs of the horizontal, complex, radial, and longitudinal tears were 0.761, 0.850, 0.601, and 0.858, respectively.
conclusionOur study showed that the CNN model has the potential to be used in diagnosing the presence of meniscal tears and differentiating the types of meniscal tears.
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