Paper 47: Deep Learning Accurately Predicts Supraspinatus Pathology on Shoulder Magnetic Resonance Imaging
Authors: Ingebritsen Ryan , Grogan Brian , Cotter Eric , Baer Geoffrey , Polce Evan 
Published: August 2023

Objectives: Rotator cuff tears (RCT) are common injuries that increase in frequency with age. Deep learning is a subfield of artificial intelligence that uses artificial neural networks to perform a variety of tasks, including image analysis. Several previous studies have investigated the utility of deep learning for identifying fatty infiltration, muscle atrophy, and tear size of the supraspinatus muscle on shoulder magnetic resonance imaging (MRI). However, limited data exists on the use of deep learning for image analysis to determine the presence of supraspinatus pathology versus normal anatomy on shoulder MRI. RadImageNet, a recently published database consisting of 1.35 million annotated computed tomography (CT), MRI, and ultrasound (US) images from greater than 130,000 patients, contains thousands of shoulder MRI images. No research to date has investigated whether deep learning algorithms can accurately detect supraspinatus pathology on MRIs in the RadImageNet database. The objective of the present study was to determine if deep learning algorithms can accurately differentiate shoulder MRI images as those with supraspinatus pathology versus normal anatomy in the RadImageNet database. Methods: The RadImageNet database was queried for all MRI images containing supraspinatus pathology. Both supraspinatus tears (partial and full thickness) and tendinosis images are categorized under the same “supraspinatus pathology” label in the database. In the comparison cohort with normal anatomy, coronal and sagittal images capturing the supraspinatus muscle were included. Although the supraspinatus was still visualized, all sagittal images medial to the glenoid were excluded to better represent the supraspinatus pathology images. The training/validation/test datasets were created using a random 80/10/10 split of the entire cohort. Data were pre-processed using standard data augmentation, including random cropping, rotation, and translation of images. ConvNeXt, a pre-trained deep learning model for the purposes of image analysis, was chosen as the base architecture for transfer learning to optimize both accuracy and computing speed. The ConvNeXt model was trained using training (N=4,937 images) and validation (N=619 images) sets for 15 epochs. Algorithm performance was assessed on an independent test cohort (N=619 images) not used during model training. Performance metrics included classification accuracy, sensitivity, specificity, and positive and negative predictive values. Model training was performed in PyTorch on an 8-gigabyte NIVIDIA Quadro M4000 Cloud Graphics Processing Unit. Results: A total of 5,241 shoulder MRIs with supraspinatus pathology and 932 without any pathology were obtained for comparison. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classifier on the independent test set was 98.9% (95% confidence interval: 97.7% – 99.5%), 98.7%, 100.0%, 100.0%, and 93.1%, respectively. A confusion matrix for model predictions on the test set is displayed in Figure 1. Interpretation plots displaying six examples of incorrect model predictions are shown in Figure 2. Class 1 and 2 designate images without pathology and those with supraspinatus pathology, respectively. All six images were cases where the model predicted “normal” when the ground truth label of the image was supraspinatus pathology. Figure 3 demonstrates an example of class activation mapping, which visually indicates the areas on an image that influenced the model prediction the most. Bright yellow coloration indicates highly influential regions whereas purple coloration corresponds to regions that were less influential. In Figure 3, which is an example image of supraspinatus pathology, one of the most influential regions appears to localize to the supraspinatus tendon. Conclusions: Deep learning is a viable method for differentiating supraspinatus pathology from normal anatomy on shoulder MRIs. The ConvNeXt model demonstrated excellent performance in predicting images with supraspinatus pathology on shoulder MRIs in the RadImageNet database. Future research using deep learning techniques to classify different rotator cuff tear types and sizes is warranted.