Clinical evaluation of Artificial Intelligence Driven Remote Monitoring technology for assessment of patient oral hygiene during orthodontic treatment.

INTRODUCTION: This study aimed to clinically evaluate the accuracy of Dental Monitoring's (DM) artificial intelligence (AI) image analysis and oral hygiene notification algorithm in identifying oral hygiene and mucogingival conditions. METHODS: Twenty-four patients seeking orthodontic therapy were monitored by DM oral hygiene protocol during their orthodontic treatment. During the bonding appointment and at each of 10 subsequent adjustment visits, a total of 232 clinical oral examinations were performed to assess the presence of the 3 oral hygiene parameters that DM monitors. In each clinical timepoint, the subjects took an oral DM scan and received a notification regarding their current oral status at that moment in time. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated to evaluate AI and clinical assessment of plaque, gingivitis, and recession. RESULTS: A total of 232 clinical time points have been evaluated clinically and by the DM AI algorithm. For DM's AI detection of plaque and calculus, gingivitis, and recession, the sensitivity was 0.53, 0.35, and 0.22; the specificity was 0.94, 0.96, and 0.99; and the accuracy was 0.60, 0.49, and 0.72, respectively. CONCLUSIONS: DM's oral hygiene notification algorithm has low sensitivity, high specificity, and moderate accuracy. This indicates a tendency of DM to underreport the presence of plaque, gingivitis, and recession.

In-vivo evaluation of Artificial Intelligence Driven Remote Monitoring technology for tracking tooth movement and reconstruction of 3-dimensional digital models during orthodontic treatment.

INTRODUCTION: An in-vivo evaluation of the Dental Monitoring (DM; Paris, France) Artificial Intelligence Driven Remote Monitoring technology was conducted in an active clinical setting. Our objectives were to compare the accuracy and validity of the 3-dimensional (3D) digital models remotely generated from the DM application to 3D Digital Models generated from the iTero Element 5D intraoral scanner (Align Technologies, San Jose, Calif) of patients' dentition during in-vivo fixed orthodontic treatment. METHODS: The orthodontic treatment of 24 patients (aged 14-55 years) was tracked across an average of 13.4 months. Scans of the maxillary and mandibular arches of each patient were taken by an iTero intraoral scanner and with the DM application before treatment initiation without (T0) and with (T1) the fixed orthodontic appliances and at every in-person adjustment appointment (T2-T10). The global deviation between the reconstructed digital models from the DM and iTero scans was compared at each time point using Geomagic Control-X 2020 (3D Systems, Rock Hill, SC). Descriptive analysis was conducted to determine the mean deviation at each time point for the maxillary and mandibular arches, to compare the maxilla and mandible mean deviations at each time point to the null hypothesis mean of 0 mm and the paired mean of the average at each time point between the maxilla and mandible. RESULTS: The findings revealed no clinically significant difference between the reconstructed digital models generated by the iTero IOS and the remotely reconstructed digital dental models generated by the DM application. CONCLUSION: DM artificial intelligence tracking algorithm can track tooth movement and reconstruct 3D digital models to a clinically acceptable degree for orthodontic application.

Effectiveness of AI-driven remote monitoring technology in improving oral hygiene during orthodontic treatment.

OBJECTIVE: This study aimed to evaluate the effectiveness of Dental Monitoring™ (DM™) Artificial Intelligence Driven Remote Monitoring Technology (AIDRM) technology in improving the patient's oral hygiene during orthodontic treatment through AI-based personalized active notifications. METHODS: A prospective clinical study was conducted on two groups of orthodontic patients. DM Group: (n = 24) monitored by DM weekly scans and received personalized notifications on the DM smartphone application regarding their oral hygiene status. Control Group (n = 25) not monitored by DM. Both groups were clinically assessed using Plaque Index (OPI) and the Modified Gingival Index (MGI). DM Group was followed for 13 months and the Control Group was followed for 5 months. Student-independent t test and paired t tests were used to investigate the mean differences between study groups and between time points for each group respectively. RESULTS: At all time points, the mean differences indicated that the DM group had lower OPI and MGI values than the control group. The mean value for OPI and MGI were statistically significantly lower in the DM group (OPI = 1.96, MGI = 1.56) than in the control group (OPI = 2.41, MGI = 2.17) after 5 months. A rapid increase in mean OPI and MGI values was found between T0 and T1 for both study groups. A plateau effect for OPI scores appeared to occur from T1 to T5 for both study groups, but the plateau effect seemed to be more pronounced for the DM group than the study group. The MGI values for both study groups also increased dramatically from baseline to T5, however, a plateau effect was not observed. CONCLUSIONS: The oral hygiene of orthodontic patients rapidly worsens over the first 3 months and plateaus after about 5 months of treatment. AIDRM by weekly DM scans and personalized active notifications may improve oral hygiene over time in orthodontic patients.