European journal of orthodontics · 2026
Automated prediction of hand-wrist maturation stage from lateral cephalometric and panoramic radiographs using a deep learning approach.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Edik Merve, Çelebi Fatma, Etöz Meryem
Аннотация
OBJECTIVES: This study aimed to predict hand-wrist developmental stages, the most reliable indicator of skeletal maturation, from panoramic radiographs (PRs) and lateral cephalometric radiographs (LCRs) using deep learning and to compare the two methods. The goal was to provide a rapid and objective method for growth assessment in orthodontic planning without additional radiation exposure.
METHODS: A total of 3703 patients who underwent hand-wrist radiographs (HWRs), PRs, and LCRs prior to orthodontic treatment were retrospectively analyzed. HWRs were classified according to Björk's 9-stage system and reduced to 5- and 3-group schemes. YOLOv5 was used for automated reference point detection, and the extracted regions were classified using a multimodal deep learning framework. EfficientNet-B0 was used for 9-stage classification, and EfficientNet-B4 was used for 5- and 3-group classifications. Clinical variables (age, gender) were included, and hybrid loss strategies addressed class imbalance while preserving sequential stage relationships. Performance metrics included accuracy, recall, mAP@0.5, balanced accuracy, Cohen's Kappa coefficient, and detailed error analysis; 5-fold cross-validation and ensemble learning were used to improve robustness.
RESULTS: YOLOv5 detected tooth roots in PRs (accuracy 92.5%, recall 93.3%, mAP@0.5 = 0.942) and cervical vertebrae in LCRs (accuracy 98.9%, recall 99.9%, mAP@0.5 = 0.995). In PRs, the standard accuracy in 9-, 5-, and 3-stage classifications was 0.3648, 0.5307, and 0.7520, respectively, while in LCRs, the standard accuracy in 9-, 5-, and 3-stage classifications was 0.4350, 0.6336, and 0.8975, respectively.
CONCLUSION: Deep learning can reliably automate skeletal maturation assessment across imaging methods and resolutions. Appropriately optimized models provide valuable decision support in orthodontic treatment, increasing efficiency and consistency.
METHODS: A total of 3703 patients who underwent hand-wrist radiographs (HWRs), PRs, and LCRs prior to orthodontic treatment were retrospectively analyzed. HWRs were classified according to Björk's 9-stage system and reduced to 5- and 3-group schemes. YOLOv5 was used for automated reference point detection, and the extracted regions were classified using a multimodal deep learning framework. EfficientNet-B0 was used for 9-stage classification, and EfficientNet-B4 was used for 5- and 3-group classifications. Clinical variables (age, gender) were included, and hybrid loss strategies addressed class imbalance while preserving sequential stage relationships. Performance metrics included accuracy, recall, mAP@0.5, balanced accuracy, Cohen's Kappa coefficient, and detailed error analysis; 5-fold cross-validation and ensemble learning were used to improve robustness.
RESULTS: YOLOv5 detected tooth roots in PRs (accuracy 92.5%, recall 93.3%, mAP@0.5 = 0.942) and cervical vertebrae in LCRs (accuracy 98.9%, recall 99.9%, mAP@0.5 = 0.995). In PRs, the standard accuracy in 9-, 5-, and 3-stage classifications was 0.3648, 0.5307, and 0.7520, respectively, while in LCRs, the standard accuracy in 9-, 5-, and 3-stage classifications was 0.4350, 0.6336, and 0.8975, respectively.
CONCLUSION: Deep learning can reliably automate skeletal maturation assessment across imaging methods and resolutions. Appropriately optimized models provide valuable decision support in orthodontic treatment, increasing efficiency and consistency.