부산대학교 도서관

Calcaneal fracture is the most common type of tarsal fractures, accounting for about 60%; calcaneus radiograph is the primary medical imaging choose for calcaneal fractures due to high accessibility, lower radiation dose compared to computed tomography, and lower cost. Artificial intelligence has made significant progress in medical imaging in recent years. The purpose of this study is to train an artificial intelligence model with a deep learning convolutional neural network to automatically classify whether there is a calcaneal fracture in lateral view of ankle radiograph. Retrospective collection of 1126 cases of lateral view of ankle radiographs, including 299 cases of fractures and 827 cases of non-fractures, was annotated by two radiologists and imaging pre-processed for model training. The training, validation, and test datasets are allocated 8:1:1. The model architecture is ShuffleNet-v2-x0.5, and the chest X-ray data set of the National Institutes of Health is used as pre-training. The accuracy rate in the validation data set is 100.000%, and the accuracy rate in the test data set is 97.321%. There are 27 true positives, 82 true negatives, 0 false positives, and 3 false negatives. The area under the curve was 0.996. The precision is 1.000, the recall is 0.900, and the F1 score is 0.947. It is confirmed that the convolutional network of deep learning can perform well in medical imaging. This model may be used as a preliminary screening or auxiliary diagnosis to assist radiologists in image interpretation.