## 4 Discussion and Conclusions  
In order to summarize the methods, Table 1 shows the main feature values of every study. Starting from this table, we mapped each value with the criteria already outlined from 1 to 5, showed in Table 2 and Figure 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper N\({}^{\circ}\)** & **AUC** & **Accuracy** & **Dataset Size** & **Sensitivity** & **Specificity** & **Labelling** \\ \hline Dimililer [9] & NA & B: 94\% & \begin{tabular}{c} TR: 30 \\ TE: 70 \\ \end{tabular} & NA & NA & NA \\ [PERSON] et al. [10] & B: 0.97 & NA & \begin{tabular}{c} P-TR: 100,855 \\ TR: 34,990 \\ TE: 4900 \\ \end{tabular} & B: 93\% & B: 94\% & Y: \textgreater{}15 \\ [PERSON] et al. [13] & B: 0.99 & B: 95\% & \begin{tabular}{c} TR: 1702 \\ TE: 189 \\ \end{tabular} & M: 85\% & B: 97\% & Y: \textgreater{}15 \\  & M: 0.94 & M: 76\% & \begin{tabular}{c} TE: 189 \\ TE: 25,645 \\ \end{tabular} & NA & M: 94\% & NA\textgreater{} \\ [PERSON] et al. [15] & NA & B: 83\% & \begin{tabular}{c} TR: 179,521 \\ TE: 25,645 \\ \end{tabular} & NA & M: 94\% & NA\textgreater{} \\  & & & & & N: 1 \\ [PERSON] et al. [17] & B: 0.95 & NA & \begin{tabular}{c} TR: 111 \\ TE: 239 \\ \end{tabular} & B: 90\% & B: 88\% & Y: 3 \\  & & & & C: 2 \\ [PERSON] et al. [19] & NA & B: 96\% & \begin{tabular}{c} TR: 96 \\ TE: 24 \\ \end{tabular} & NA & NA & Y: NA \\  & & & & C: 1 \\ [PERSON] et al. [21] & B: 0.98 & B: 91\% & \begin{tabular}{c} TR: 3605 \\ TE: 100 \\ \end{tabular} & B: 98\% & B: 84\% & NA\textgreater{} \\ [PERSON] et al. [24] & B: 0.98 & NA & \
\begin{tabular}{c} TR: 111 \\ TE: 100 \\ \end{tabular} & B: 96\% & B: 94\% & Y: 3 \\  & & & & C: 2 \\  & & & & N: \textgreater{}1 \\ [PERSON] et al. [26] & B: 0.93 & NA &
\begin{tabular}{c} TR: 36,808 \\ TE: 556 \\ \end{tabular} & B: 81\% & B: 89\% & Y: 9 \\  & & & C: 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the six different features of fracture classification methods, related to the corresponding paper. Legend: not assessable (NA); pre-training (P-TR); training (TR); testing (TE); binary (B); multi-class (M); number of specialists (N); average years of expertise (Y); number of subsequent checks (C). Starting from these values, we were able to put together all the pros found in the different articles and define what should be the correct approach to tackle this task. Most of the work focused on classification between broken and unbroken bones, without extending the task to different types of fractures. We think that a generalized tool, able to distinguish different types of fractures, should follow the classification stated by the AO foundation. The AO classification is hierarchical and is determined by the localization and configurations of the fracture lines, where each bone is divided in subsequent sub-groups of fracture, as shown in Figure 3 for the case of proximal femur. In the common literature, the AO classification was claimed to present a better reproducibility compared to other classification systems [31] and its configuration made it optimal for a classification task. Plus, the structure is the same for different bones in the human body, so the approach could be easily extended. Once the correct classification system is defined, an adequate dataset is certainly one of the most important aspects for a deep learning-based application to operate efficiently. Even if, in some studies, good results have been obtained for the fracture/no-fracture classification without using a large dataset [9; 19], a correct number of images is suggested when the network has to distinguish between different sub-groups of fractures. The dataset could be increased and balanced with data augmentation techniques, if needed, but without adding useless or misleading information. For example, using shear, strain or spot noise augmentation could cause a normal bone image to be classified as a bone with a fracture [19]. Thus, data augmentation is not always enough to balance irregular datasets.