This study deploys the deep learning-based object detection algorithms to detect midfacial fractures in computed tomography (CT) images. The object detection models were created using faster R-CNN and RetinaNet from 2,000 CT images. The best detection model, faster R-CNN, yielded an average precision of 0.79 and an area under the curve (AUC) of 0.80. In conclusion, faster R-CNN model has good potential for detecting midfacial fractures in CT images.
Keywords: Facial trauma; artificial intelligence; deep learning; midfacial fracture.