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arXiv · 2408.09894

Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network

Chris Hyunchul Jo·Jiwoong Yang·Byunghwan Jeon·Hackjoon Shim·Ikbeom Jang
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Abstract

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.

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Chris Hyunchul Jo, Jiwoong Yang, Byunghwan Jeon, Hackjoon Shim, Ikbeom Jang. 2024-08-19. Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network. https://arxiv.org/abs/2408.09894

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