###### Abstract  
Ankle fractures are common and, compared to other injuries, tend to be overlooked in the emergency department. We aim to develop a deep learning algorithm that can detect not only definite fractures but also obscure fractures. We collected the data of 1226 patients with suspected ankle fractures and performed both X-rays and CT scans. With anteroposterior (AP) and lateral ankle X-rays of 1040 patients with fractures and 186 normal patients, we developed a deep learning model. The training, validation, and test datasets were split in a 3/1/1 ratio. Data augmentation and under-sampling techniques were administered as part of the preprocessing. The Inception V3 model was utilized for the image classification. Performance of the model was validated using a confusion matrix and the area under the receiver operating characteristic curve (AUC-ROC). For the AP and lateral trials, the best accuracy and AUC values were 83%/0.91 in AP and 90%/0.95 in lateral. Additionally, the mean accuracy and AUC values were 83%/0.89 for the AP trials and 83%/0.9 for the lateral trials. The reliable dataset resulted in the CNN model providing higher accuracy than in past studies.  
ankle fractures; computed tomography; deep learning: artificial intelligence; convolutional neural network +
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human brain. Convolutional neural networks (CNN) is a widely used deep learning algorithm for data processing, especially for 2D images [5].  
Previous studies have successfully applied CNN to detect fractures via radiographs [6; 7; 8]. [PERSON] et al. [9], designed a CNN algorithm using pelvic radiographs that could detect a femoral neck fracture with 97% accuracy; the algorithm represented other types of fractures with decreasing accuracy thereby rendering itself inadequate for the purpose of our study.  
To the best of our knowledge, there are two studies on ankle fractures using deep learning. [PERSON] et al. [10] used data from structured reports of X-ray images of ankle fractures. The dataset included 157 patients, of which 129 revealed fractures and 28 without, and the model exhibited an accuracy of 77% with the area under curve (AUC) being 0.85. [PERSON] et al. [11] performed an extensive study on a larger dataset of 596 images (ankle with and without fractures equally apportioned) with five different CNN architectures; the accuracy peaked at 81%.  
Both studies are proof of concept research; hence, we considered conducting a more practical study. Large or definite fractures can be easily diagnosed even by a beginner; minor fractures wherein the fracture line is obscured or overlapping are more difficult to detect in the ED as well as the outpatient department as shown in Figures 1 and 2. Hence, the aim of our study is not only to distinguish definite fracture but also not to overlook vague minor fractures in the radiography. To this effect, we tried to involve the patient data of minor fractures as well as definite fracture where possible. For accurate labeling, we reviewed both X-rays and computed tomography (CT) scans of all the fractures. Additionally, a machine learning expert (Mo, Y-C.) was consulted to achieve higher performance and accuracy of the proposed model. Our study is the first of its kind in that we labeled the data almost perfectly by means of images of X-rays and CT scans.  
Figure 1: Large or definite fracture. Tibia shaft fracture. It can be easily identified, even by a novice.