Detecting upper extremity native joint dislocations using deep learning: A multicenter study.

Objective: Joint dislocations are orthopedic emergencies that require prompt intervention. Automatic identification of these injuries could help improve timely patient care because diagnostic delays increase the difficulty of reduction. In this study, we developed convolutional neural networks (CNNs) to detect elbow and shoulder dislocations, and tested their generalizability on external datasets.

Methods: We collected 106 elbow radiographs (53 with dislocation [50 %]) and 140 shoulder radiographs (70 with dislocation [50 %]) from a level-1 trauma center. After performing 24× data augmentation on training/validation data, we trained multiple CNNs to detect elbow and shoulder dislocations, and also evaluated the best-performing models using external datasets from an external hospital and online radiology repositories. To examine CNN decision-making, we generated class activation maps (CAMs) to visualize areas of images that contributed the most to model decisions.

Results: On all internal test sets, CNNs achieved AUCs >0.99, and on all external test sets, CNNs achieved AUCs >0.97. CAMs demonstrated that the CNNs were focused on relevant joints in decision-making regardless of whether or not dislocations were present.

Conclusion: Joint dislocations in both shoulders and elbows were readily identified with high accuracy by CNNs with excellent generalizability to external test sets. These findings suggest that CNNs could expedite access to intervention by assisting in diagnosing dislocations.