Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910570262822912 |
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| author | Jo, Chris Hyunchul Yang, Jiwoong Jeon, Byunghwan Shim, Hackjoon Jang, Ikbeom |
| author_facet | Jo, Chris Hyunchul Yang, Jiwoong Jeon, Byunghwan Shim, Hackjoon Jang, Ikbeom |
| contents | Research question: We test whether a plane shoulder radiograph can be used together with deep learning methods to identify patients with rotator cuff tears as opposed to using an MRI in standard of care. Findings: By integrating convolutional block attention modules into a deep neural network, our model demonstrates high accuracy in detecting patients with rotator cuff tears, achieving an average AUC of 0.889 and an accuracy of 0.831. Meaning: This study validates the efficacy of our deep learning model to accurately detect rotation cuff tears from radiographs, offering a viable pre-assessment or alternative to more expensive imaging techniques such as MRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network Jo, Chris Hyunchul Yang, Jiwoong Jeon, Byunghwan Shim, Hackjoon Jang, Ikbeom Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Research question: We test whether a plane shoulder radiograph can be used together with deep learning methods to identify patients with rotator cuff tears as opposed to using an MRI in standard of care. Findings: By integrating convolutional block attention modules into a deep neural network, our model demonstrates high accuracy in detecting patients with rotator cuff tears, achieving an average AUC of 0.889 and an accuracy of 0.831. Meaning: This study validates the efficacy of our deep learning model to accurately detect rotation cuff tears from radiographs, offering a viable pre-assessment or alternative to more expensive imaging techniques such as MRI. |
| title | Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.09894 |