Background: In the dental field, digital technology has created new opportunities for orthodontists to integrate their clinical practice, and for patients to collect information about orthodontics and their treatment, which is called “teledentistry.” Dental monitoring (DM) is a recently introduced orthodontic application that combines safe teledentistry with artificial intelligence (AI) using a knowledge-based algorithm, allowing an accurate semi-automatic monitoring of the treatment. Dental Monitoring is the world’s first SaaS (Software as a Service) application designed for remote monitoring of dental treatment, developed in Paris, France, with Philippe Salah as the Co-founder and CEO. Cases presentation: This report describes two cases in which DM system was essential to achieve the control of certain movements: it was possible to follow the movement, even if complex, such as the anterior cross of an adult patient and a lack of space in the canine of the growing patient. The software analyzed the fit and retention of the aligner, thus ensuring correct biomechanics. They were treated during the COVID-19 pandemic lockdown with aligners. The first case is a growing patient who was monitored during an interceptive orthodontic treatment to manage a retained upper canine. The second case is an adult patient forced to finalize his treatment of upper lateral incisor crossbite. The software analyzed the fit and retention of the aligner, thus ensuring correct biomechanics. Conclusions: DM system appears to be a promising method, useful for improving the interaction between doctor and patient, generally acceptable and useful to patients, even in critical clinical situations, at least in cases with optimal compliance and ability to use the tool properly.

Introduction: This study aimed to test the performance of DentalMonitoring’s (DM) artificial intelligence (AI) in detecting aligner tracking issues. Methods: This multicenter retrospective comparative study analyzed 3,323 assessments from 623 patients treated at multiple U.S. sites. DM’s AI performance was evaluated using a binary model (seated vs. unseated) and a three-level model (seated, slight unseat, noticeable unseat). AI outputs were compared against a reference standard established through independent case reviews performed by a panel of three U.S.-based orthodontic residents. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Results: For the binary comparison (seat vs. unseat), sensitivity was 93.2% and specificity 86.2%, with a PPV of 89.2% and an NPV of 94.4%. For the three-level comparison, the noticeable-unseat category demonstrated a sensitivity of 91.1% and a specificity of 90.5%, with a PPV of 66.1% and an NPV of 98.3%. The high NPV values across both models indicate that DM’s AI was particularly reliable in ruling out clinically meaningful unseat events. The lower PPV in the noticeable-unseat category reflects the low prevalence of noticeable unseats in the dataset. Conclusion: DM’s AI system demonstrated high sensitivity and negative predictive values in identifying unseat events and in differentiating noticeable from slight unseats within the positive subset. These results indicate that the model performed reliably within the parameters and dataset evaluated, particularly in minimizing false-negative assessments of clinically meaningful misfits. Further validation in independent cohorts and across broader clinical contexts is warranted to confirm generalizability.