Abstract
Category:
Ankle; Trauma
Introduction/Purpose:
Weightbearing CT (WBCT) scan provides an ability to compare the ankle joints bilaterally in a 3D manner under physiologic load. According to our recent investigations, 3D volume measurement of the syndesmosis, if measured up to 5cm proximal to the tibial plafond, can detect the instability with an accuracy of 90%, sensitivity of 95.8%, and specificity of 83.3%. However, these values can differ based on the knowledge and experience of the human interpreter. Deep learning, as a subset of machine learning, has shown promising potentials in processing and analyzing images and detecting abnormalities within the images using deep convolutional neural networks (DCNN). Herein, we aimed to assess the accuracy, sensitivity, and specificity of 3D volume WBCT evaluation using DCNN algorithms in patients with subtle syndesmotic instability.
Methods:
In this study 140 bilateral ankle WBCT scans of patients with subtle syndesmotic instability who were diagnosed intraoperatively were allocated to the patient group. The control group comprised 140 bilateral ankle WBCT images of healthy individuals. We utilized inception V3 model for our DCNN. Data augmentation and transfer learning were used; however, the images were not preprocessed in terms of change in size and resolution. The data were divided as 80:10:10 for training, validation, and test subsets, respectively. The outcome of the study was expressed as sensitivity, specificity, F-score, and the area under the curve (AUC).
Results:
The performance of our DCNN algorithm showed a sensitivity of 99.41%, specificity of 99.34%, F-score of 99.37%, and 99.99% AUC (Figure 1). The change in loss value of the train data was plateaued after 40 iterations. Axial images were the most appropriate images that were used by the algorithm to detect the instability.
Conclusion:
In this study we observed that using DCNN in the process of WBCT image interpretation for diagnosis of syndesmotic instability, particularly in subtle cases, makes this modality almost perfect with a very small chance of missing a case. Training a DCNN using a greater number of inputs is still recommended to improve the validity and reliability of this method. Providing a heat map will also help clinicians discover the process of decision-making by these algorithms as DCNNs are sometimes called 'black box'.
