Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network
<|reference_start|>Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network: 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.<|reference_end|>
@article{jo2024preoperative,
    title={Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs
  using a Convolutional Block Attention Module-Integrated Neural Network},
    author={Chris Hyunchul Jo, Jiwoong Yang, Byunghwan Jeon, Hackjoon Shim, Ikbeom
  Jang},
    journal={arXiv preprint arXiv:2408.09894},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09894},
    primaryClass={eess.IV cs.AI cs.CV}
}