Deep Learning-Based Classification of Mandibular Growth-Rotation Phenotypes from Lateral Cephalometric Radiographs: A Comparative Evaluation of Convolutional Neural Networks

Background/Objectives: Prediction of mandibular growth rotation is important for orthodontic diagnosis and treatment planning, but conventional cephalometric methods are subjective and clinician-dependent. Deep learning may improve diagnostic consistency and efficiency. This study aimed to develop and evaluate a model for predicting mandibular growth rotation patterns from lateral cephalometric radiographs. Methods: A total of 800 untreated lateral cephalometric radiographs from individuals aged 9–20 years were retrospectively collected and classified as clockwise (n = 376) or counter-clockwise (n = 424) growth rotation patterns. The dataset was divided into training (80%) and validation (20%) sets. Three pre-trained convolutional neural network architectures (ResNet50, InceptionV3, and VGG16) were trained and compared. Image preprocessing included noise reduction, intensity normalization, and data augmentation. Model performance was evaluated using accuracy, precision, recall, F1-score, validation loss, and confusion matrix analysis. Results: VGG16 demonstrated the best performance, achieving the highest validation accuracy and lowest validation loss among the tested models. The model correctly predicted clockwise growth rotation with an accuracy of 84.57% and counter-clockwise growth rotation with an accuracy of 75.26%. Overall classification accuracy was 81.12%. Precision values were 0.85 and 0.73, recall values were 0.67 and 0.88, and F1-scores were 0.75 and 0.80 for counter-clockwise and clockwise growth patterns, respectively. Conclusions: The proposed VGG16-based model demonstrated promising accuracy in predicting mandibular growth rotation patterns from lateral cephalometric radiographs. Deep learning may serve as a valuable adjunct to orthodontic diagnosis and treatment planning, with further improvements possible through larger datasets and enhanced computational resources.