Deep learning model for measurement of shoulder critical angle and acromion index on shoulder radiographs

BACKGROUND: Several bone morphological parameters, including the anterior acromion morphology, the lateral acromial angle, the coracohumeral interval, the glenoid inclination, the acromion index (AI), and the shoulder critical angle (CSA), have been proposed to impact the development of rotator cuff tears and glenohumeral osteoarthritis. This study aimed to develop a deep learning tool to automate the measurement of CSA and AI on anteroposterior shoulder radiographs. METHODS: We used MURA Dataset v1.1, which is a large publicly available musculoskeletal radiograph dataset from the Stanford University School of Medicine. All normal shoulder anteroposterior radiographs were extracted and annotated by an experienced orthopedic surgeon. The annotated images were divided into train (1004), validation (174), and test (93) sets. We use pytorch_segmentation_models for U-Net implementation and PyTorch framework for training the model. The test set was used for final evaluation of the model. RESULTS: The mean absolute error for CSA and AI between human-performed and machine-performed measurements on the test set with 93 images was 1.68° (95% CI 1.406°-1.979°) and 0.03 (95% CI 0.02 - 0.03), respectively. CONCLUSIONS: A deep learning model can precisely and accurately measure CSA and AI in shoulder anteroposterior radiographs. A tool of this nature makes large-scale research projects feasible and holds promise as a clinical application if integrated with a radiology software program.