Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image
Abstract Background Deep 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 classificat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Musculoskeletal Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12891-022-05468-6 |
| _version_ | 1811257034864066560 |
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| author | Hyunkwang Shin Gyu Sang Choi Oog-Jin Shon Gi Beom Kim Min Cheol Chang |
| author_facet | Hyunkwang Shin Gyu Sang Choi Oog-Jin Shon Gi Beom Kim Min Cheol Chang |
| author_sort | Hyunkwang Shin |
| collection | DOAJ |
| description | Abstract Background Deep 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. Methods We 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. Results The 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. Conclusion Our 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. |
| first_indexed | 2024-04-12T17:50:10Z |
| format | Article |
| id | doaj.art-9db99a6f183c4f7183dbccfeecc32782 |
| institution | Directory Open Access Journal |
| issn | 1471-2474 |
| language | English |
| last_indexed | 2024-04-12T17:50:10Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Musculoskeletal Disorders |
| spelling | doaj.art-9db99a6f183c4f7183dbccfeecc327822022-12-22T03:22:32ZengBMCBMC Musculoskeletal Disorders1471-24742022-05-012311910.1186/s12891-022-05468-6Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance imageHyunkwang Shin0Gyu Sang Choi1Oog-Jin Shon2Gi Beom Kim3Min Cheol Chang4Department of Information and Communication Engineering, Yeungnam UniversityDepartment of Information and Communication Engineering, Yeungnam UniversityDepartment of Orthopedic Surgery, Yeungnam University College of Medicine, Yeungnam UniversityDepartment of Orthopedic Surgery, Yeungnam University College of Medicine, Yeungnam UniversityDepartment of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam UniversityAbstract Background Deep 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. Methods We 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. Results The 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. Conclusion Our 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.https://doi.org/10.1186/s12891-022-05468-6Deep learningConvolutional neural networkMagnetic resonance imagingMeniscus tearKnee |
| spellingShingle | Hyunkwang Shin Gyu Sang Choi Oog-Jin Shon Gi Beom Kim Min Cheol Chang Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image BMC Musculoskeletal Disorders Deep learning Convolutional neural network Magnetic resonance imaging Meniscus tear Knee |
| title | Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image |
| title_full | Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image |
| title_fullStr | Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image |
| title_full_unstemmed | Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image |
| title_short | Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image |
| title_sort | development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image |
| topic | Deep learning Convolutional neural network Magnetic resonance imaging Meniscus tear Knee |
| url | https://doi.org/10.1186/s12891-022-05468-6 |
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