Automatic Identification and Segmentation of Orbital Blowout Fractures Based on Artificial Intelligence

Abstract

Purpose: The incidence of orbital blowout fractures (OBFs) is gradually increasing due to traffic accidents, sports injuries, and ocular trauma. Orbital computed tomography (CT) is crucial for accurate clinical diagnosis. In this study, we built an artificial intelligence (AI) system based on two available deep learning networks (DenseNet-169 and UNet) for fracture identification, fracture side distinguishment, and fracture area segmentation. Methods: We established a database of orbital CT images and manually annotated the fracture areas. DenseNet-169 was trained and evaluated on the identification of CT images with OBFs. We also trained and evaluated DenseNet-169 and UNet for fracture side distinguishment and fracture area segmentation. We used cross-validation to evaluate the performance of the AI algorithm after training. Results: For fracture identification, DenseNet-169 achieved an area under the receiver operating characteristic curve (AUC) of 0.9920 ± 0.0021, with an accuracy, sensitivity, and specificity of 0.9693 ± 0.0028, 0.9717 ± 0.0143, and 0.9596 ± 0.0330, respectively. DenseNet-169 realized the distinguishment of the fracture side with accuracy, sensitiv-ity, specificity, and AUC of 0.9859 ± 0.0059, 0.9743 ± 0.0101, 0.9980 ± 0.0041, and 0.9923 ± 0.0008, respectively. The intersection over union (IoU) and Dice coefficient of UNet for fracture area segmentation were 0.8180 ± 0.0093 and 0.8849 ± 0.0090, respectively, showing a high agreement with manual segmentation. Conclusions: The trained AI system could realize the automatic identification and segmentation of OBFs, which might be a new tool for smart diagnoses and improved efficiencies of three-dimensional (3D) printing-assisted surgical repair of OBFs. Translational Relevance: Our AI system, based on two available deep learning network models, could help in precise diagnoses and accurate surgical repairs.