Quantitative evaluation of an artificial intelligence-driven remote monitoring system for occlusion assessment using patient-captured images.

Objectives: To evaluate the accuracy of an artificial intelligence (AI) model developed by DentalMonitoring for assessing occlusal parameters from patient-acquired intraoral images, using intraoral scanner (IOS)-derived three-dimensional (3D)measurements as the reference standard. Materials and Methods: This multicenter prospective study included 430 orthodontic patients from three clinics in the United States. Each participant completed a DentalMonitoring scan using the DM ScanBox and a clinician-acquired IOS scan. Midline deviation, overbite, overjet, and canine class were measured on IOS-generated 3D models using metrology-grade software (ZEISS Inspect). Three independent, blinded technicians performed measurements, with the median value used as the reference. Agreement between AI-generated and reference measurements was assessed using Passing-Bablok regression and relative bias analyses at predefined clinical thresholds. Results: All occlusal parameters demonstrated agreement within clinically acceptable limits. Midline deviation and overbite showed the highest concordance, with intercepts near 0.00 mm, relative biases below 3%, and mean biases of -0.01 ± 0.26 mm and -0.04 ± 0.39 mm, respectively. Overjet was modestly overestimated (mean bias = +0.29 ± 0.52 mm), while canine class showed increasing underestimation at higher values (mean bias = -0.31 ± 0.91 mm). Conclusions: The evaluated AI model demonstrated high agreement with IOS-based 3D measurements for midline deviation and overbite, with greater variability for overjet and canine classification. These results support the use of AI-assisted monitoring for screening and follow-up, while highlighting the need for further validation prior to routine clinical implementation.