A Novel Deep Learning Approach for Cervical Vertebral Maturation Classification

Abstract Objectives This study aims to automatically determine the cervical vertebral maturation staging (CVM) on lateral cephalometric radiograph images using a customized deep convolutional neural network (DCNN) model and to evaluate the classification performance using a custom DCNN model in detecting CVM stages. Methods A dataset of 922 digital lateral cephalometric radiographs from individuals aged 7–20 years was collected. Image quality was assessed for artifacts and clarity of C2-C4 vertebrae. CVM staging was independently performed by two orthodontists, with inter-observer reliability assessed using kappa coefficient. Image pre-processing involved random oversampling to address class imbalance and resizing to 128x128 pixels. A custom convolutional neural network was developed, with hyperparameters optimized using random search. The final architecture comprised convolutional layers, global average pooling, dense layers, and dropout. The model was trained for 50 epochs using Adam optimizer and categorical cross-entropy loss. Performance evaluation included accuracy, loss, and confusion matrix analysis on a validation set. Results A novel convolutional neural network was developed for the classification of CVM staging. This custom model initially exhibited overfitting, achieving perfect training accuracy but only 57% validation accuracy due to class imbalance. Implementing Random Oversampling (ROS) addressed this issue by balancing the dataset. Hyperparameter tuning optimized the model architecture, resulting in a final validation accuracy of 85.96%. The model demonstrated strong performance in classifying CVMS 1, 2, and 3, with precision and recall exceeding 95%. However, classification of CVMS 4 and 5 posed challenges, with lower precision and recall values. Overall accuracy reached 88.2%, indicating a generally robust model, though further improvements are necessary for CVMS 5. Conclusion This study successfully developed a custom deep convolutional neural network for automated cervical vertebral maturation (CVM) staging on lateral cephalometric radiographs. By addressing class imbalance and optimizing hyperparameters, the model achieved a validation accuracy of 88.2%. While demonstrating potential for clinical application, the model’s performance varied across CVM stages, indicating a need for further refinement to improve accuracy and robustness.