## 7 Conclusions  
By developing a new method for localizing cephalometric landmarks, we solved a concrete problem from industry in this research. The existing methods have been adapted and tested to detect only 19 landmarks; however, in our work we have addressed the problem of detecting 72 cephalometric landmarks based on industry needs. A large number of accurately detected landmarks on skull X-ray images is a prerequisite for any quality cephalometric analysis. In this study, we upgraded the SpatialConfiguration-Net neural network (SCN), which is one of the state-of-the-art methods for localizing cephalometric landmarks in X-ray images. The SCN architecture was modified by the integration of several repetitions of simpler local appearance and spatial configuration components, with which we increased the capacity of such a modified network (i.e., the SCN-EXT network) with virtually unchanged degrees of freedom (DoF) compared to the original SCN network with the more complex components. Primarily, the SCN-EXT network was designed for localizing a large number of cephalometric landmarks in diverse skull X-ray images. On the small ISBI public database with 250 testing images, captured by the same X-ray device and with 19 cephalometric landmarks, our, albeit non-tuned SCN-EXT method, was, in terms of effectiveness, just slightly behind the state-of-the-art methods. On the other hand, our fine-tuned SCN-EXT method was statistically significantly the most accurate method on the much more demanding AUDAX database with 4695 highly variable testing images (various X-ray devices!) and with 72 cephalometric landmarks. The improvement of the proposed method was statistically significant, even if we considered out of all 72 cephalometric landmarks only those 19 landmarks that are also in the ISBI database. We also confirmed that the detection accuracy was correlated positively with the importance of landmarks for cephalometric analyses. An aim of this research was indeed to develop a state-of-the-art cephalometric landmark detection method, but not at the expense of a raw increase of neural network capacity by increasing DoF (e.g., by the addition of more filters, etc.). The presented results in this study were, namely, obtained by using the SCN-EXT network, which had 13% (on the AUDAX database) or 33% (on the ISBI database) fewer free parameters than the original SCN network. Maintaining DoF while increasing network capacity is important, especially for a small learning set and limited computer resources. Possible improvements to our approach are seen in the use of a more sophisticated augmentation of learning set and in the use of transfer learning. We expect, reasonably, also an improvement in the case if we integrate \(J=9\) or more repetitions of the local appearance and spatial configuration components to the SCN-EXT network, which would indeed increase DoF greatly. For the sake of a fair comparison with the state-of-the-art methods, we have not conducted any of the abovementioned in this study, so these may provide guidelines for future research. In addition to lateral skull X-ray images, we also have an option of capturing frontal skull X-ray images. This is complementary information that allows complementary cephalometric analyses. One of the future research directions will, therefore, be focused on adapting our method for also localizing cephalometric landmarks on the frontal skull X-ray images. Finally, let us mention that our detection algorithm is already employed in a clinical practice as a part of a bigger software product. Accurately determined landmarks on the skull X-ray images represent the input for every cephalometric analysis. Automatic localization of 72 cephalometric landmarks undoubtedly disburdens the orthodontist greatly, as manual detection of landmarks means routine and time-consuming work. Nevertheless, he should be aware that, similar to other software tools in clinical practice, our algorithm also does not work 100% accurately.