Deep learning framework for automated cervical vertebral maturation staging using CBCT scans

Automated skeletal maturity assessment is essential in orthodontic diagnosis and treatment planning, guiding the optimal timing of interventions such as myofunctional appliances and orthognathic surgery. This study aims to develop and evaluate deep learning algorithms for the assessment and classification of cervical vertebral maturation (CVM) stages using sagittal views derived from cone beam computed tomography (CBCT) scans. The sample consisted of 364 CBCT scans of orthodontic patients from private practices in the midwestern United States. The CVM stages were classified by two orthodontists and an oral and maxillofacial radiologist. Distinct experimental settings evaluated image-based classification, prompt-guided anatomical localization using the Segment Anything Model (SAM), demographic conditioning with age and sex information, knowledge distillation from a teacher network pretrained on a skeletal maturity dataset, and tri-modal input fusion. Because data availability differed across settings, these components were evaluated on different eligible subsets rather than as a unified sequential ablation. In the full 364-sample six-class baseline setting, ResNet34 achieved 66.76% accuracy. In the separate 96-sample, stage-aligned five-class knowledge-distillation setting, ResNet50 achieved the highest observed accuracy of 88.54% (5-fold cross-validation average), compared with 87.10% for its non-distilled counterpart in the stage-aligned five-class knowledge-distillation setting using a paired subset of 96 scans. The distillation effect varied across architectures, and the 96-sample subset was small and class-imbalanced. The findings support the feasibility of deep learning-based CVM staging from CBCT-derived sagittal views across distinct experimental settings. The results do not establish that anatomical guidance, demographic conditioning, and knowledge distillation jointly improve classification because these components were not evaluated through a unified ablation on a common cohort. Within the separate five-class knowledge-distillation setting, performance changes were backbone-dependent, with only a modest numerical improvement observed for ResNet50. The findings should therefore be considered preliminary and internally validated. Larger, balanced, and externally validated cohorts are required before broader clinical applicability can be established.