AI-Driven Quantitative Dental Imaging: A Clinical Framework for Assessing Root Resorption Across Treatment Modalities.

Background

Orthodontically induced external root resorption (ERR) may affect long-term tooth stability, requiring a reliable assessment of root length changes. This study developed an artificial intelligence (AI)-based framework for automatic segmentation and quantitative evaluation of root resorption, comparing fixed appliances and clear aligner therapy.

Methods

A dataset of 100 anonymised orthopantomographic (OPG) radiographs (50 fixed appliance, 50 clear aligner) obtained before and after orthodontic treatment was analysed. A U-Net model was trained for automatic tooth segmentation and quantitative assessment of tooth length changes. Measurements were computed from both AI-predicted and Dentist-annotated masks using pixel-based Python analysis (Python version 3.6), and pre-post differences were compared between methods.

Results

The segmentation model achieved high performance with Intersection over Union values up to 89% and Dice Similarity Coefficients up to 95%. Quantitative analysis demonstrated significant reductions in root length following orthodontic treatment in both modalities (p < 0.0001). In the fixed appliance group, AI-based measurements showed an average root length reduction of 8.95%, while human measurements indicated a reduction of 10.16%. In the clear aligner group, AI measurements demonstrated a reduction of 2.81%, compared with 4.98% in human measurements. Root resorption was significantly greater in the fixed appliance group than in the clear aligner group (p < 0.0001). AI-derived measurements showed strong agreement with expert evaluations.

Conclusions

AI-based analysis of panoramic radiographs may provide a reliable and reproducible approach for quantifying orthodontically induced root resorption. The findings suggest that clear aligner therapy was associated with lower root length reduction; however, larger multi-centre studies and external validation are required before clinical implementation.