Precisión de la inteligencia artificial de DentalMonitoring en la detección de emergencias comunes relacionadas con...

This study aimed to evaluate the diagnostic performance of DentalMonitoring™ (DM) (Dental Monitoring SAS, Paris, France), an FDA-cleared AI-powered orthodontic remote monitoring software, in detecting three common orthodontic appliance issues: bracket debonding, open self-ligating clips, and tie loss. Datasets from 1,014 US-based patients were analyzed. Each DM image set was assessed by the AI algorithm and independently reviewed by a panel of three orthodontic experts, whose consensus served as the reference standard. A total of 659 evaluations were included for bracket debonding, 647 for self-ligating clip status, and 653 for tie presence. Sensitivity and specificity along with their 95% confidence intervals were calculated using a two-level evaluation basis (positive vs. negative) across the three clinical parameters. DM's AI demonstrated high diagnostic performance, with sensitivity of 98.4% for bracket debonding, 91.1% for open self-ligating clips, and 93.3% for tie loss. Corresponding specificity values were 99.6%, 88.3%, and 96.5%, respectively. Current results indicate that DM's AI analysis system has high accuracy in detecting bracket debonding, open self-ligating clips, and tie loss. DM can help significantly reduce the rate of these undetected clinical incidents, providing a better approach to managing emergencies and maintaining clinical control in orthodontic treatment.