A unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT

Abstract

To develop and validate an artificial intelligence (AI)-driven tool for the automatic segmentation of pulp cavity structures in maxillary premolars teeth on cone-beam computed tomography (CBCT). One hundred and eleven CBCT scans were divided into training (n = 55), validation (n = 14), and testing (n = 42) sets, with manual segmentation serving as the ground truth. The AI tool automatically segmented the testing dataset, with errors corrected by an operator to create refined 3D (R-AI) models. The overall AI performance was assessed by comparing AI and R-AI models, and thirty percent of the test sample was manually segmented to compare AI and human performance. Time-efficiency of each method was recorded in seconds (s). Statistical analysis included independent and paired t-tests to evaluate the effect of tooth type on accuracy metrics and AI versus manual segmentation. One-way ANOVA with Tukey’s post hoc test was used for time efficiency analysis. A 5% significance level was used for all analyses.The AI tool demonstrated excellent performance with Dice similarity coefficients (DSC) ranging from 88% ± 7 to 93% ± 3 and 95% Hausdorff distances (HD) from 0.13 ± 0.06 to 0.16 ± 0.06 mm. Automated segmentation of maxillary second premolars performed slightly better than that of maxillary first premolars in terms of intersection over union (p = 0.005), DSC (p = 0.008), recall (p = 0.008), precision (p = 0.02), and 95% HD (p = 0.04). The AI-based approach showed higher recall (p = 0.04), accuracy (p = 0.01), and lower 95% HD than manual segmentation (p < 0.001). AI segmentation (42.8 ± 8.4 s) was 75 times faster than manual segmentation (3218.7 ± 692.2 s) (p < 0.001). The AI tool proved highly accurate and time-efficient, surpassing human expert performance.