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

• URL: http://arxiv.org/abs/2408.09894v1
• Date: Mon, 19 Aug 2024 11:08:49 GMT
• Title: Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network
• Authors: Chris Hyunchul Jo, Jiwoong Yang, Byunghwan Jeon, Hackjoon Shim, Ikbeom Jang,
• Abstract summary: We test whether a plane shoulder radiograph can be used together with deep learning methods to identify patients with rotator cuff tears. By integrating convolutional block attention modules into a deep neural network, our model demonstrates high accuracy in detecting patients with rotator cuff tears.
• Score: 0.04590531202809992
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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