## 4 Discussion  
The introduction of AI to the fields of medicine, dentistry, and, in particular, orthodontics is now the object of scientific study due to its many potential applications which could help operators in their clinical practice. One such AI application is remote monitoring of patients during orthodontic treatment with both clear aligners and fixed multibracket appliances. Dental Monitoring(r) (DM, Paris, France) is the company leading remote monitoring using ADIRM technology. DM's algorithm extracts information from intra-oral pictures and compares it to a baseline intra-oral model, thereby providing information about tooth movement during treatment [19]. This software has been proven to be able to reduce chairside appointments and to detect cross-bite, linear tooth movements, aligner fit, and patients' oral hygiene status [16,20]. Recent clinical reviews have demonstrated that Dental Monitoring(r) not only reduces the number of in-office visits, but also contributes to improved patient oral hygiene and post-treatment retention stability. Patients under AI-driven remote monitoring showed significantly lower plaque index levels and better retainer fit. These findings suggest that DM can enhance treatment quality without compromising clinical outcomes [34]. In addition, both the effectiveness of DM software in tracking dental movements and the precision of AI-generated intra-oral models has already been demonstrated [25]. Another advantage the software provides to clinicians is the opportunity to download intra-oral models taken by patients on their smartphones through the DM application during orthodontic treatment. These can then be used by the operator for the fabrication of orthodontic devices without the need for an in-office appointment. In this study, the null hypothesis was that there would be no differences between measurements made on C.stl and AI.stl renderings taken at the same time point during orthodontic treatment. The results showed good correspondence between the two sets of measurements, with the exception of molar areas on both upper and lower arches, specifically on the palatal side. The differences between measurements of the distance between the thickest areas of the gingival margin (Z6) were statistically significant, being 0.39 (\(p\) = 0.002) in the upper arch and 0.26 (\(p\) = 0.014) in the lower. Therefore, the null hypothesis was refuted. It should be noted that the generation of intra-oral models from 2D photographic inputs--such as those used by AI-driven platforms like Dental Monitoring--relies on image  
Figure 5: Scatter plot of lower MVC6 measurements. X axis: C.stl data; Y axis: AI.stl data. sequences captured by patients under uncontrolled conditions. This process, unlike intra-oral scanning performed in a clinical setting, may introduce small geometric inaccuracies due to factors such as inconsistent lighting, movement, or angulation. These aspects may be particularly relevant when assessing curved anatomical regions, and those where soft tissue interference may be more pronounced [25], a likely explanation for the discrepancies noted at posterior regions in this study. Indeed, this area has been proven to suffer a distortion during the intra-oral scanning process, resulting in slight inaccuracy of the intra-oral model [35; 36; 37]. Therefore, some inaccuracy of AI-generated intra-oral models can be expected in posterior areas. Indeed, in this study, the differences in mm between measurements taken on C.stl and AI.stl at Z6 was 0.39 (\(p\) = 0.002) in the upper arch and 0.26 (\(p\) = 0.014) in the lower. However, although these discrepancies were statistically significant, if we take into account the threshold set by the American Board of Orthodontists, 0.5 mm, they appear not to be clinically significant [38]. Previous studies have reached similar conclusions, that is, that DM-generated intra-oral models are as accurate as those taken on conventional digital ones [24; 25].