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. 2023 Apr 3;12(4):7.
doi: 10.1167/tvst.12.4.7.

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

Xiao-Li Bao  1 , Xi Zhan  2 , Lei Wang  3 , Qi Zhu  1 , Bin Fan  1 , Guang-Yu Li  1
Affiliations

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

Xiao-Li Bao et al. Transl Vis Sci Technol. .

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, sensitivity, 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.

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Conflict of interest statement

Disclosure: X. Bao, None; X. Zhan, None; L. Wang, None; Q. Zhu, None; B. Fan, None; G.-Y. Li, None

Figures

Figure 1.
Figure 1.
Architectures of DenseNet-169 and UNet. (A) DenseNet-169 included various convolutional layers. Each convolutional layer contained the output of all the previous convolutional layers. (B) UNet was composed of contracting (down-sampling) and expanding paths (up-sampling). In the process of down-sampling, 3 × 3 valid convolution operation and rectified linear unit (ReLU) activation were repeated twice to reduce image resolution. The expansion path also contained 4 blocks, each containing 3 × 3 deconvolutions, and the ReLU function.
Figure 2.
Figure 2.
Selection of network models . (A) The receiver operating characteristic curves of DenseNet for fracture identification. (B) The receiver operating characteristic curves of ResNet for fracture identification. (C) The receiver operating characteristic curves of AlexNet for fracture identification. (D) The receiver operating characteristic curves of VGGNet for fracture identification. AUC, area under the curve.
Figure 3.
Figure 3.
Evaluation of post-trained DenseNet-169 for fracture identification. (A) The receiver operating characteristic curve of DenseNet-169 for fracture identification. (B) The precision-recall curve of DenseNet-169 for fracture identification. (C) The convergence of the loss function of DenseNet-169 during the training process for fracture identification. AUC, area under the curve; ROC, receiver operating characteristic.
Figure 4.
Figure 4.
Evaluation of post-trained DenseNet-169 for fracture side distinguishment. (A) The receiver operating characteristic curve of DenseNet-169 for fracture side distinguishment. (B) The precision-recall curve of DenseNet-169 for fracture side distinguishment. (C) The convergence of the loss function of DenseNet-169 during the training process for fracture side distinguishment. AUC, area under the curve; ROC, receiver operating characteristic.
Figure 5.
Figure 5.
Manual and AI segmentation for various types of orbital blowout fractures. AI, artificial intelligence.
Figure 6.
Figure 6.
Manual and AI-segmentation located in the infraorbital canal/infraorbital foramen area.
See this image and copyright information in PMC

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