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 m...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Chris Hyunchul Jo, Yang, Jiwoong, Jeon, Byunghwan, Shim, Hackjoon, Jang, Ikbeom
Format: Article
Language:English
Subjects:
Artificial neural networks
Deep learning
Identification methods
Imaging techniques
Machine learning
Modules
Radiographs
Online Access:Get full text
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Summary: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.
ISSN:2331-8422