| Title: |
Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network |
| Authors: |
Jo, Chris Hyunchul, Yang, Jiwoong, Jeon, Byunghwan, Shim, Hackjoon, Jang, Ikbeom |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2408.09894 |
| Accession Number: |
edsarx.2408.09894 |
| Database: |
arXiv |
