## 4 Discussion
For instance, [PERSON] and colleagues [24] studied the accuracy of AI-generated intra-oral models on typodonts and found statistically, but not clinically, significant differences in both maxillary and mandibular arches; the discrepancies were <0.5 mm, and, therefore, below the threshold set by the ABO [38]. Subsequently, [PERSON] et al. published an in vivo study on patients undergoing orthodontic treatment with fixed multibrackets appliances. Intra-oral models generated by DM and an intra-oral scanner were compared using Geomagic Control X Software 2020 (3D Systems Inc, Rock Hill, SC, USA) [25; 38]. They found statistically significant differences between conventional intra-oral models and AI-generated intra-oral ones. However, taking into account the threshold of 0.5 mm set by the ABO, no clinical discrepancies were found between the conventionally and AI-generated intra-oral models [25; 38]. That being said, the methodology relied on here differed from those used in the above studies, specifically that by [PERSON] and colleagues [25]. Firstly, the sample differed: in the present study, models from 38 patients were included, whereas [PERSON] and colleagues had 24 patients in their study group, but analyzed 466 scans and 233 superimpositions [25]. As for the method, we only measured transverse dimensions on the upper and lower arches to compare conventionally and AI-generated models, whereas [PERSON] et al. made superimpositions of conventional and DM-generated models [25]. Another factor to consider is that in the above-cited studies [24; 25], no brackets or resin grip points were present on buccal teeth surfaces. In the present study, buccal resin grip points were present when taking both DM intra-oral photos and conventional intra-oral scans. Other research has assessed the influence of orthodontic brackets and arch-wires on intra-oral scan accuracy, reporting statistical differences with minimum clinical significance [39; 40]. However, no studies assessing the influence of resin grip points on intra-oral scan accuracy are reported in the literature. Nonetheless, they are usually applied on the buccal surfaces of teeth, and in the present study only the cusp tips, grooves, and palatal/lingual sides were used for measurements. A further use for remote monitoring could be tracking palatal expansion and transverse dimension changes during the active phase of palatal expansion, and previous studies have demonstrated the reliability of DM assessment of transversal dimensions [15; 33]. [PERSON] et al. showed a good correspondence between measurements from intra-oral examination, DM analysis, and digital models. In particular, DM software was able to identify posterior cross-bite correction [33]. [PERSON] at al. compared the software scores for linear changes and measurements made on dental models [15]. They also found a good match between the measurements taken on the intra-oral models and those made by the DM software [15]. In the present investigation, transverse measurements made by the same specialist orthodontist were evaluated on digital models derived from intra-oral scanning versus those AI-generated by Dental Monitoring software. Despite the statistical differences found, the results suggest that remote monitoring with DM, i.e., the possibility of having AI-generated intra-oral models for clinical evaluation of the progress of orthodontic treatment, is feasible, taking into account that posterior areas may present some discrepancies. However, this study has some limitations. Specifically, the numbers of patients included and measurements taken were smaller than those reported by both [PERSON] and colleagues and [PERSON] et al. [24; 25]. Furthermore, as this was a retrospective study, no prior sample size analysis was performed. The inclusion and exclusion criteria were applied to consecutively treated patients but, being a retrospective study, it was not blinded. The operator who performed the measurements knew which group the models belonged to.