Deep learning-based identification and maturation assessment of the zygomaticomaxillary suture in cone-beam computed tomography images

The dual-network deep learning system can automatically locate and assess the maturation of the zygomaticomaxillary suture in three dimensions from CBCT files, providing a critical foundation for clinical decision-making in maxillary protraction therapy. This deep learning-model improves diagnostic workflow by reducing assessment time and minimizing inter-observer variability, providing clinicians with a reliable and reproducible reference for optimal maxillary protraction timing-avoiding over-treatment of the very young (stage A) while ensuring timely intervention before sutural closure (stages D and E).