Root resorption represents a significant clinical challenge in orthodontic practice, necessitating the development of precise diagnostic techniques. This study employed an advanced Mask RCNN-Gate Context Aggregation (GCA) deep learning algorithm to analyze volumetric characteristics of maxillary structures, specifically focusing on canines and lateral incisors in patients with unilateral impacted canines. The study aimed to identify key morphological predictors for maxillary canine impaction and establish quantitative indicators for early clinical intervention.

CBCT data were obtained from 45 patients diagnosed with unilateral maxillary canine impaction. The dataset was partitioned into a training set (32 cases, 70%) , a validation set (9 cases, 20%) , and a test set (4 cases, 10%) . Following preprocessing, which included data augmentation techniques such as random rotation, affine shear, horizontal and vertical flipping, and image translation, a total of 4 500 two-dimensional images were generated to enhance the training of the Mask RCNN-GCA model. Model performance was assessed using the Dice similarity coefficient (DSC) , sensitivity, and accuracy. Additionally, volumetric differences in maxillary bone and dental structures were compared between the impacted and non-impacted sides with t-test analysis.

The refined model demonstrated outstanding segmentation performance, attaining a DSC of 93.60% and an accuracy of 96.50% in maxillary structures; for canine identification, the values were a DSC of 92.50% and an accuracy of 97.50%. Quantitative analysis revealed statistically significant volumetric reductions (t_{maxillary volume} = 2.90, P_{maxillary volume} = 0.01; t_{canine volume} = 2.42, P_{canine volume} = 0.005; t_{lateral incisor volume} = 3.11, P_{lateral incisor volume} = 0.006) on the impacted side for maxillary bone [mean difference: (8 992 ± 1 685) mm³], canine (8 767 mm³) , and lateral incisor (3 028 mm³) compared to contralateral measurements. Gender-based analysis showed no statistically relevant differences in any measured parameters (t_{impacted sides} = 0.77, P_{impacted sides} = 0.40; t_{non-impacted sides} = 0.23, P_{non-impacted sides} = 0.91) . No significant volumetric variation was observed in first premolars (t = 0.77, P = 0.402) .

The enhanced Mask RCNN-GCA framework provided an effective solution for automated, high-precision volumetric assessment of maxillary impaction cases. The identified volumetric disparities in maxillary bone and associated dentition offered clinically relevant morphological markers for early diagnosis and intervention. These findings contributed valuable quantitative references for minimizing orthodontic complications, particularly adjacent root resorption, through timely therapeutic strategies.