Compact Involutional Transformer for Automated Detection of Pediatric Tooth Number Anomalies on Panoramic Radiographs

Pediatric tooth number anomalies can compromise occlusion, craniofacial development, and long-term treatment planning. This study introduces the Compact Involutional Transformer (CIT), a novel transformer architecture for automated detection of permanent tooth germ deficiency and supernumerary teeth on pediatric panoramic radiographs. The model is a transformer fed by an adaptive involution-based tokenizer, combining locality-aware tokenization with contextual self-attention in a compact design. Pediatric panoramic radiographs (n = 1170) were retrospectively collected from patients with completed diagnoses and treatments, and radiographic labels were curated and verified by an experienced pediatric dentist. Performance was evaluated on both multi-class (germ deficiency, normal, and supernumerary teeth) and binary tasks. In the three-class setting, CIT outperformed state-of-the-art baselines, achieving 96.00% accuracy, 95.29% F1, 95.76% ROC-AUC, and 93.28% Matthews correlation coefficient. The decision process was examined using Grad-CAM visualizations. Model predictions were benchmarked against two independent dentist cohorts with different experience levels; Bonferroni adjusted McNemar's tests (m = 5; $a_{\mathrm{adj}}$ = 0.01) indicated significantly higher diagnostic performance than the expert pediatric dentist group, while the difference from the general dentist group was not statistically significant. To our knowledge, this study presents the first AI approach for automatic detection of germ deficiency and the first application of an involution-based tokenizer within a transformer for pediatric panoramic image analysis.