Prediction of skeletal maturity using machine learning based on multiple biological indicators

Clinical Snapshot

Key Findings

• P Value: Not reported for primary comparison

• Effect Size: AUC-ROC 0.924 for CatBoost model vs baseline cervical-only model

• Primary Outcome: Skeletal maturation stage prediction accuracy

• Nnt Or Sensitivity: F1-score of 0.752, precision-recall AUC of 0.806

• Confidence Interval: Not reported

Clinical Application

High - uses standard orthodontic imaging (lateral cephalograms and panoramic radiographs) already obtained in routine practice Highly relevant to Australian orthodontic practice where reducing radiation exposure is prioritised. Could potentially reduce need for additional hand-wrist radiographs, aligning with ACPSEM radiation protection guidelines and supporting PBS-funded orthodontic assessments Adolescent and child orthodontic patients requiring skeletal maturity assessment

Abstract

INTRODUCTION: Accurate assessment of skeletal maturity is essential for orthodontic treatment planning and growth modulation. This study aimed to develop a comprehensive machine learning (ML) model to predict skeletal maturation stages using cervical vertebral morphology, dental maturation stage (DMS), gender, and age. METHODS: A total of 860 patients with lateral cephalograms, panoramic, and hand-wrist radiographs were included. A baseline model using 6 cervical morphologic parameters was compared with enhanced models incorporating DMS, gender, and age. Six ML algorithms were evaluated, with CatBoost showing the highest performance. Feature importance was analyzed using 2 methods, and the final model was assessed using a confusion matrix, receiver operating characteristic curves, and calibration curves. RESULTS: Incorporating DMS, gender, and age significantly improved predictive performance. The CatBoost model achieved an area under the receiver operating characteristic curve of 0.924, an area under the precision-recall curve of 0.806, and an F1-score of 0.752. Feature importance analysis confirmed the strong predictive contribution of DMS, especially the mandibular second molar. The final model demonstrated high accuracy, strong discrimination, and good calibration across all skeletal maturity stages. CONCLUSIONS: This study presents a novel and clinically practical ML model for skeletal maturity prediction, integrating routinely acquired orthodontic records. The results demonstrate that combining DMS with cervical morphology, age, and gender enhances prediction accuracy, offering a reliable alternative to traditional hand-wrist assessments.

References

1. 1.Guo, M., Daraqel, B., Ai, D., Ma, X., Dong, Y., Hu, Y., & Zheng, L. (2026). Prediction of skeletal maturity using machine learning based on multiple biological indicators. American Journal of Orthodontics and Dentofacial Orthopedics. https://doi.org/10.1016/j.ajodo.2025.11.020