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Clinical evaluation of Artificial Intelligence Driven Remote Monitoring technology for assessment of patient oral
Vivian Snider1, Karen Homsi1, Budi Kusnoto1
1Department of Orthodontics, College of Dentistry, University of Illinois Chicago, Chicago, Ill.
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.
