Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury
Objective: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. Methods: We presented a casc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/12/2049 |
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| author | Yingkai Ma Yong Qin Chen Liang Xiang Li Minglei Li Ren Wang Jinping Yu Xiangning Xu Songcen Lv Hao Luo Yuchen Jiang |
| author_facet | Yingkai Ma Yong Qin Chen Liang Xiang Li Minglei Li Ren Wang Jinping Yu Xiangning Xu Songcen Lv Hao Luo Yuchen Jiang |
| author_sort | Yingkai Ma |
| collection | DOAJ |
| description | Objective: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. Methods: We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. Results: C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. Conclusion: The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses. |
| first_indexed | 2024-03-11T02:34:24Z |
| format | Article |
| id | doaj.art-21a91883ef9143279274f1a842b3b23c |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T02:34:24Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-21a91883ef9143279274f1a842b3b23c2023-11-18T10:00:23ZengMDPI AGDiagnostics2075-44182023-06-011312204910.3390/diagnostics13122049Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus InjuryYingkai Ma0Yong Qin1Chen Liang2Xiang Li3Minglei Li4Ren Wang5Jinping Yu6Xiangning Xu7Songcen Lv8Hao Luo9Yuchen Jiang10Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, ChinaSecond Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, ChinaSecond Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, ChinaDepartment of Control Science and Engineering, Harbin Institute of Technology, Haerbin 150001, ChinaDepartment of Control Science and Engineering, Harbin Institute of Technology, Haerbin 150001, ChinaSecond Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, ChinaSecond Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, ChinaSecond Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, ChinaSecond Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, ChinaDepartment of Control Science and Engineering, Harbin Institute of Technology, Haerbin 150001, ChinaDepartment of Control Science and Engineering, Harbin Institute of Technology, Haerbin 150001, ChinaObjective: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. Methods: We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. Results: C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. Conclusion: The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses.https://www.mdpi.com/2075-4418/13/12/2049meniscus injuryMRIvisualcascaded-progressive convolutional neural networkdiagnosis |
| spellingShingle | Yingkai Ma Yong Qin Chen Liang Xiang Li Minglei Li Ren Wang Jinping Yu Xiangning Xu Songcen Lv Hao Luo Yuchen Jiang Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury Diagnostics meniscus injury MRI visual cascaded-progressive convolutional neural network diagnosis |
| title | Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury |
| title_full | Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury |
| title_fullStr | Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury |
| title_full_unstemmed | Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury |
| title_short | Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury |
| title_sort | visual cascaded progressive convolutional neural network c pcnn for diagnosis of meniscus injury |
| topic | meniscus injury MRI visual cascaded-progressive convolutional neural network diagnosis |
| url | https://www.mdpi.com/2075-4418/13/12/2049 |
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