## 4 Discussion  
In this demonstrative study, artificial intelligence was used, for the first time to the authors' knowledge, to perform morphometric analysis on high-resolution synchrotron-based tomographic images of peri-implant connective tissue. The overall objective was to obtain reliable quantitative information on collagen bundles and their 3D distribution. We showed that deep learning semantic image segmentation can better identify the bundles compared to a common thresholding segmentation technique [37]. Notably, we had recently found, by synchrotron imaging, the presence of transversely and longitudinally oriented (with respect to the dental implant axis) collagen bundles in the peri-implant soft tissue; these grow and organize intertwining patterns during their development at different times and in different quantitative distributions depending on environmental conditions, to date not fully understood [9]. Therefore, discriminating and quantifying transversal and longitudinal bundles turns out to be an extremely interesting step forward in understanding the wound healing process in the peri-implant soft tissue and the role of collagen in it. In this study, we demonstratively showed that a good way to find these features is to use the semantic segmentation method of synchrotron-based highly resolved tomographic images. With the support of neural networks and deep learning, it was possible to quantify structures in the samples that were not considered before. In particular, collagen bundles were identified by their orientation and not by their physical densities. This is fundamental in order to discriminate transversal and longitudinal bundles that, having the same physical density, could not have been discriminated using conventional thresholding segmentation techniques. In practice, we have managed to create a neural network able to separate longitudinal and transversal fibers by U-Net [30; 31]. Within the limits of the extremely reduced sample size, we did not detect a statistically significant difference in volume percentage and degree of anisotropy between data obtained by conventional segmentation methods based on thresholding and the innovative ones based on artificial intelligence. On the other hand, as regards the connectivity density parameter, it was observed that the data obtained in deep learning were higher for all samples than the data obtained with conventional thresholding (statistically significant in all samples except for the Ctrl_st_wt); this fact is certainly attributable to a greater discrimination of the collagen bundles and their connectivity through artificial intelligence. Based on quantitative comparison between transversal and longitudinal bundles, carried out in deep learning, we discovered an extremely interesting fact: considering all the parameters globally, a clear prevalence was observed of the quantity and connectivity of transversal bundles compared to longitudinal ones in all of the samples; moreover, transversal bundles were found to be more anisotropically oriented (i.e., they had a preferred orientation) than the longitudinal ones. These differences were, in most cases, statistically significant and were confirmed by polarized light microscopy observations. This evidence could indicate that in the wound healing process in the peri-implant area, the transverse fibers are formed first, and only subsequently the longitudinal ones. In reality, having found in the Ctr2_lt_no-wt, investigated after permanence in vivo for 10 years, the same quantitative mismatch between transversal and longitudinal fibers as in the other samples, we could reasonably exclude that the lower presence of longitudinal fibers compared to transversal ones was due to the limited healing time. Thus, in general, we can state that, even in a mature and functional peri-implant connective tissue, the number of longitudinal fibers is always lower than the number of transversal ones; this information is completely new and has never been observed in the literature.