Automatic Detection of Perilunate and Lunate Dislocations on Wrist Radiographs Using Deep Learning

Delayed or missed diagnosis of perilunate or lunate dislocations can lead to significant morbidity. Advances in computer vision provide an opportunity to improve diagnostic performance. In this study, a deep learning algorithm was used for detection of perilunate and lunate dislocations on lateral wrist radiographs. A total of 435 lateral wrist radiographs were labeled as normal or pathologic (perilunate or lunate dislocation). The lunate in each radiograph was segmented with a rectangular bounding box. Images were partitioned into training and test sets. Two neural networks, consisting of an object detector followed by an image classifier, were applied in series. First, the object detection module was used to localize the lunate. Next, the image classifier performed a binary classification for normal or pathologic. The accuracy, sensitivity, and specificity of the overall system were evaluated. A receiver operating characteristic curve and the associated area under the curve were used to demonstrate the overall performance of the computer vision algorithm. The lunate object detector was 97.0% accurate at identifying the lunate. Accuracy was 98.7% among the subgroup of normal wrist radiographs and 91.3% among the subgroup of wrist radiographs with perilunate/lunate dislocations. The perilunate/lunate dislocation classifier had a sensitivity (recall) of 93.8%, a specificity of 93.3%, and an accuracy of 93.4%. The area under the curve was 0.986. The authors have developed a proof-of-concept computer vision system for diagnosis of perilunate/lunate dislocations on lateral wrist radiographs. This novel deep learning algorithm has potential to improve clinical sensitivity to ultimately prevent delayed or missed diagnosis of these injuries.