## 4 Discussion  
Clinicians, especially in the ED, tend to overlook minor fractures of ankle due to the busy environment in the emergency room; this happens until they have had sufficient experience. Thus, we tried to determine whether machine learning algorithms could help diagnose these fractures. The primary intent of our study was to analyze the feasibility of using AI techniques for identifying ankle fractures. This was achieved using the following criteria: (1) The model should be able to correctly detect minor fractures as well as major fractures; (2) the performance of the model should either equal or surpass human diagnosis. We concluded that the study, in its present capacity, fell short on both aspects. Other studies on pelvic, wrist, and other fractures showed over 90% classification accuracy and an AUC of 0.95 [16; 17; 18]. Compared with the previous two studies on ankle fractures using deep learning, our study showed improved accuracy (i.e., 83% > 76%) [10; 11]. Therefore, we conducted further analysis on the results obtained from our study. First, this study is the first ever to use both X-rays and images of CT scans of ankle fractures for a labeling process; previous studies have utilized only X-rays. Moreover, the images were reviewed by three medical professionals and hence we can confirm that our datasets are reliable. However, they are also quite imbalanced in that there were relatively less normal images per abnormal ones, as patients who underwent CT scans were primarily concerned with ankle fractures. Even though we used an under-sampling technique, it is a major limitation of our study. Second, apart from class imbalance, we encountered one other major issue during the experiments that was associated with artifacts, especially splint. Among the X-rays that  
Figure 7: (a) ROC curve—AP trial. (b) ROC curve—lateral trial. (c) ROC curve—best trained models. had splint, the majority featured fractures, but the model, nonetheless, classified all images with splint as fracture (i.e., Figure 8). Because there is no study published in this regard, this issue needs to be studied further as splint is used to treat a wide range of injuries. It is still unclear how to train a model to classify X-rays with splint. Third, minor fractures were occasionally accompanied by large fractures; such multiple fractures may cause the model to misunderstand the only large fracture as \"abnormal\" while attempting to categorize the minor fracture as \"normal\". To resolve this problem, we plan to apply an object detection algorithm in our future study, as shown in Figure 9. The object detection model can not only classify but also localize where the lesion is in images with a bounding box (red line on Figure 9) in the dataset. Lastly, owing to insufficient normal images in the datasets, the hyper-parameter could not be efficiently fine-tuned despite employing under-sampling and data augmentation techniques. Given the various challenges we encountered in the machine learning tasks, it is difficult to determine precisely why the AP trial demonstrated higher accuracy as compared to the lateral trial, considering that images of the lateral type have more overlapping parts. It seems to be resolved by applying \"Explainable AI\", which is a promising field and shows decision-making processes using various algorithms [19; 20]. Using this, we aim to train models to make smarter decisions and more accurate predictions in our next study. In summary, our ultimate objective is developing a robust AI framework that can help to detect fractures and convey supporting information with an accuracy of over 90%. We intend for the application to be scaled to accommodate CT, MRI, and pediatric patients as well as to not only reduce medical accidents but also assist medical professionals and doctors in their evaluations and treatment plans. Figure 8: Non-fracture X-ray with splint. Even though it is normal, in the test session, the deep learning model interpreted X-ray with splint as fractured group.