Diagnostic Accuracy of a Machine Learning Model for Cervical Vertebra-Based Skeletal Maturity Assessment in Pediatric and Adolescent Patients Using Cephalometric Radiographs

Abstract

Background/Objectives: Skeletal maturity assessment is essential for timing orthodontic growth-modification treatment. The cervical vertebral maturation (CVM) method is widely used, but manual staging is subjective and prone to inter-observer variability, and artificial intelligence (AI) may improve its consistency and accuracy. The aim of this study was to develop and externally validate a fully automated deep-learning pipeline for CVM assessment and to determine its diagnostic accuracy for three-phase and six-stage classification against calibrated expert staging. Methods: A cross-sectional three-stage deep-learning pipeline was trained and internally cross-validated on 523 cephalometric radiographs from King Abdulaziz University. Images were CLAHE-enhanced; YOLOv8 detected the C2–C4 region; a ResNet-50 classifier assigned the growth phase (Early, Peak, Late); and phase-specific binary classifiers assigned CVM stages (CS1–CS6). External validation used the independent ‘Aariz dataset (n = 150). Results: The detector achieved mAP@0.5 = 0.9939 and mean IoU = 0.8884. On external validation, the three-phase classifier reached 96.0% accuracy, macro-F1 0.919, weighted κ 0.951 and ROC-AUC 0.998, whereas the end-to-end six-stage cascade reached 79.3% accuracy, macro-F1 0.735, weighted κ 0.910 and ROC-AUC 0.923. Most errors occurred between adjacent stages, with 98.7% of predictions within ±1 stage of the reference standard. Conclusions: The automated pipeline showed promising performance for three-phase CVM classification and moderate performance for six-stage classification on one independent external benchmark. Further multicenter prospective validation is required to establish its generalizability and clinical utility.