Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network
This offers a potential pre-assessment or alternative to MRI for diagnosing rotator cuff tears, which could reduce costs and improve accessibility in medical imaging.
The study tackled the problem of predicting rotator cuff tears from shoulder radiographs using a deep learning model, achieving an average AUC of 0.889 and accuracy of 0.831.
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.