AUTOMATED SKELETAL MATURITY STAGING FROM HAND-WRIST RADIOGRAPHS USING DEEP LEARNING: A DECISION SUPPORT SYSTEM BASED ON FISHMAN’S INDICATORS
KOREAN JOURNAL OF ORTHODONTICS, cilt.11, sa.1, ss.101-105, 2026 (Hakemli Dergi)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 11 Sayı: 1
- Basım Tarihi: 2026
- Dergi Adı: KOREAN JOURNAL OF ORTHODONTICS
- Sayfa Sayıları: ss.101-105
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
Introduction: This study aims to develop an automated system using deep learning (DL) from hand-wrist radiographs based on Fishman's method for skeletal maturity staging.
Methods: A total of 2,318 hand-wrist radiographs from patients aged 8 to 19 years were retrospectively analyzed. Each radiograph was labeled by experts according to Fishman’s method. This assessment is based on 11 Skeletal Maturity Indicators (SMIs) across six anatomical regions, including the sesamoid bone, the third and fifth fingers, and the radius.Five different DL models (Faster R-CNN, FCOS, RetinaNet, SSD, and YOLOv7) were trained and evaluated. Model performance was assessed using commonly accepted metrics, including mean average precision (mAP), accuracy, precision, recall, and F1-score. Additionally, k-fold cross-validation (k=5) was applied to ensure the robustness of the results.
Results: The models achieved mAP values ranging from 0.81 to 0.93, indicating effective detection of the relevant SMIs. Faster R-CNN demonstrated the highest overall performance (mAP: 0.93; accuracy: 0.73; precision: 0.74; recall: 0.99; F1-score: 0.83). For individual SMIs, the Faster R-CNN model achieved classification accuracies above 0.81 for most indicators, except for MP3=, MP5=, and MP5cap. A similar trend was also observed for precision, recall, and F1-score. Ground truth annotations and model predictions were also presented for visual comparison.
Conclusions: All evaluated DL models, particularly Faster R-CNN, showed strong potential for reliable and automated detection of Fishman’s SMIs. This approach may serve as a valuable clinical decision support tool for orthodontists in growth assessment and treatment planning.